Professor Tarik Bourouina

Professor Giovanni Chierchia

Professor Hugues Talbot

Xudong Jiang

Ai -Qun

Dr Yuzhi Shi

Dr Kim Nguyen

Dr Truc

Thi Nguyen

Dr Hailong Thanh

Dr Jingbo Li

Dr Yu Zhang

Dr Yang Jiaqing

Mr Liu

Natesan Backiam

Miss Lipika Prakash

Mr Bin Mahato

Dr Shilun Li

Dr Junfeng Feng

Dr Ping Wu

Dr Su

Change Lim

Dr Jun Nong

Zou

Ahmed Mr

Mazen Elsayed

Erfan

Dr Yixin Wang

Dr Hui Dong

Dr Jun Li

Dr Ying Sun

Dr Wah Kong

Dr Juanjuan Wan

Mr Hu

Sing Beng

Mr Raymond Tan

Keh

Keywords: Deep learning, Bioparticle classification, Convolution neural network, Deep metric learning, Bioparticle sizing, Bioparticle images

Imaging flow cytometry is a high-throughput tool widely used for bioparticle analysis in various applications. However, the vast number of images generated by imaging flow cytometry imposes a great challenge for data analysis. Although various learning algorithms were optimized to achieve high predication accuracies, they overlooked the trade-off between speed and hardware requirements. This causes a major hurdle for mass deployment of these learning algorithms to commercial devices for high-throughput bioparticle analysis due to its high cost and high-power consumption. Moreover, rare bioparticle detection is still a significant challenge because the representative training images are hard to collect and the input images in inference may be different from the training images. Furthermore, the size measurement algorithms do not always lead to accurate sizing without size level calibration. In this thesis, we have developed an efficient neural network named MCellNet for a rapid, accurate and high-throughput detection of label-free Cryptosporidium, Giardia and other pollutants captured by imaging flow cytometry. MCellNet achieved a classification accuracy over 99.6% and a processing speed of 346+ images per second on an embedded platforms, outperforming MobileNetV2 (251 frames per second) with a similar classification accuracy. In addition, deep metric learning for rare bioparticle detection was also studied. Deep metric learning based classification algorithm enhanced the accuracy with traditional deep-learningbased features as it encodes more semantic information into the network, thus deep metric learning performs better than simple classification of deep features and it is well adapted for inlier bioparticle detection. Furthermore, a machine-learning-based pipeline was established for the high accuracy bioparticle sizing. The pipeline iii consisted of an image segmentation module for measuring the pixel size of the bioparticle and a machine learning model for accurate pixel-to-size conversion. The sizing algorithm showed significantly more accuracy and promised greater potential in a wide range of bioparticle sizing applications. The proposed methods could also be potentially applied to other high-throughput and real-time bioparticle analysis for biomedical diagnosis, environmental monitoring, and other bioparticle detection applications.

Résumé

L'imagerie par cytométrie de flux est un outil à très haute cadence largement utilisé pour l'analyse des bioparticules. Néanmoins, le grand nombre d'images générées pose un grand défi pour l'analyse rapide des données. Bien que divers algorithmes d'apprentissage aient été optimisés pour atteindre une précision de prédiction élevée, ils ont négligé le compromis entre la vitesse et les exigences matérielles, ce qui a causé un obstacle majeur au déploiement de masse de ces algorithmes d'apprentissage sur des appareils commerciaux pour l'analyse des bioparticules à haut débit, ceci en raison de son coût et sa consommation d'énergie élevée. Dans cette thèse, nous avons développé un réseau neuronal efficace, appelé MCellNet, pour une approche rapide, précise et à haut débit pour la détection de bioparticules sans marquage fluorescent, adapté à la cytométrie de flux. MCellNet a atteint une précision de classification de plus de 99,6% et une vitesse de traitement de plus de 346 images par seconde dans les plates-formes intégrées, surpassant MobileNetV2 (251 images par seconde) avec une précision de classification similaire. En outre, l'apprentissage métrique profond pour la détection de bioparticules rares a également été étudié. En outre, un pipeline basé sur l'apprentissage automatique est établi pour la classification en taille et à haute Table 2-

List of Figures

List of Tables

Motivation

Flow cytometry is one of the most widely adopted technologies for bioparticle analysis in disease diagnostics [START_REF] Van Dongen | Minimal residual disease diagnostics in acute lymphoblastic leukemia: need for sensitive, fast, and standardized technologies[END_REF][START_REF] Kanegane | Flow cytometry-based diagnosis of primary immunodeficiency diseases[END_REF][START_REF] Jennings | Recent advances in flow cytometry: application to the diagnosis of hematologic malignancy[END_REF], food inspection [START_REF] Seo | Rapid detection of Escherichia coli O157: H7 using immuno-magnetic flow cytometry in ground beef, apple juice, and milk[END_REF][START_REF] Buzatu | An integrated flow cytometry-based system for real-time, high sensitivity bacterial detection and identification[END_REF], and water quality monitoring [START_REF] Gillespie | Assessing microbiological water quality in drinking water distribution systems with disinfectant residual using flow cytometry[END_REF][START_REF] Helmi | Assessment of flow cytometry for microbial water quality monitoring in cooling tower water and oxidizing biocide treatment efficiency[END_REF][START_REF] Safford | Flow cytometry applications in water treatment, distribution, and reuse: A review[END_REF] among other bioparticle detection applications [START_REF] Adan | Flow cytometry: basic principles and applications[END_REF][START_REF] Zucker | Detection of TiO2 nanoparticles in cells by flow cytometry[END_REF][START_REF] Shapiro | Practical flow cytometry[END_REF][START_REF] Mckinnon | Flow cytometry: an overview[END_REF][START_REF] Macey | Flow cytometry[END_REF].

Conventional flow cytometry identifies bioparticles according to bioparticle optical signatures such as fluorescent profiles [START_REF] Adan | Flow cytometry: basic principles and applications[END_REF]. Bioparticle such as cells are often immunolabeled with fluorescent tags that target cell-specific biomarkers to facilitate analysis. Fluorescent labelling may alter cell properties and interfere with downstream analyses [START_REF] Marrison | Ptychography-a label free, high-contrast imaging technique for live cells using quantitative phase information[END_REF], and no knowledge of cell morphology may be derived from the information-sparse data obtained using conventional flow cytometry.

Morphology is an important characteristic feature of bioparticles. These images are packed with rich information which could be used for bioparticle classification [3,[START_REF] Blasi | Label-free cell cycle analysis for high-throughput imaging flow cytometry[END_REF][START_REF] Kobayashi | Label-free detection of cellular drug responses by highthroughput bright-field imaging and machine learning[END_REF][START_REF] Wu | Label-free bioaerosol sensing using mobile microscopy and deep learning[END_REF][START_REF] Nitta | Intelligent image-activated cell sorting[END_REF][START_REF] Gorocs | A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples[END_REF][START_REF] Li | Deep cytometry: deep learning with real-time inference in cell sorting and flow cytometry[END_REF][START_REF] Peuß | Label-independent flow cytometry and unsupervised neural network method for de novo clustering of cell populations[END_REF]. Gold standards primarily rely on bioparticles morphology with microscopy-based imaging techniques for microbial identification [START_REF] Nketia | Analysis of live cell images: Methods, tools and opportunities[END_REF]. For instance, pathogenic protozoans in drinking water, Cryptosporidium and Giardia as shown in Figure 1-1, are detected by analysing the morphology in microscopy images [START_REF] Digiorgio | Cryptosporidium and Giardia recoveries in natural waters by using environmental protection agency method 1623[END_REF].

However, the traditional microscopy-based imaging approach requires labourintensive sample preparation and time-consuming manual data analysis, which significantly hinders its application in high-throughput bioparticles analysis [START_REF] Vesey | Application of flow cytometric methods for the routine detection of Cryptosporidium and Giardia in water[END_REF].

Imaging-based flow cytometry is a high-throughput image acquisition

Chapter 1 Introduction

technology by coupling imaging techniques with flow cytometry. Imaging flow cytometry has a significantly higher throughput than traditional microscopy-based imaging techniques and is capable of capturing thousands to millions of bioparticle images in a second using various imaging modalities [START_REF] Han | Imaging technologies for flow cytometry[END_REF]. The acquired images are packed with rich information on bioparticle morphologies. Nevertheless, the vast number of information-rich images imposes a great challenge for data analysis.

Machine learning is often used to identify bioparticles such as cells, planktons and microalgae during image analysis [START_REF] Ruske | Machine learning for improved data analysis of biological aerosol using the WIBS[END_REF][START_REF] Rösch | On-line monitoring and identification of bioaerosols[END_REF]. Except for a few cases where machine learning is used for image correction, enhancement and reconstruction [START_REF] Gorocs | A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples[END_REF], a number of machine learning algorithms have been developed for imaging-based cell detection and classification [5,[START_REF] Wu | Label-free bioaerosol sensing using mobile microscopy and deep learning[END_REF][START_REF] Meng | Large-scale multi-class image-based cell classification with deep learning[END_REF][START_REF] Gӧrӧcs | A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples[END_REF][START_REF] Zhang | Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning[END_REF][START_REF] Kim | Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells[END_REF] with reported high accuracy in these works, but they were relatively slow and required high computation resources beyond the capability of embedded systems in order to build affordable imaging flow cytometry machine.

For instance, a highly accurate algorithm based on a sophisticated densely connected neural network [START_REF] Huang | Densely connected convolutional networks[END_REF] for high-throughput cancer cell detection in blood was reported [5]. It shows a good detection performance, but its speed was limited to 100 fps, and it relies on a high-performance platform with high-end Nvidia GeForce GTX 1080Ti GPU. Intelligent image-activated cell sorting 2.0 achieve a higher throughput cell sorting up to 2000 events per seconds, but requires a 8-PC server with 8 multicore CPUs and GPUs (NVIDIA GeForce GTX 1080 Ti) for image processing with deep learning [4]. Most existing machine learning algorithms developed for imaging flow cytometry primarily focus on the detection accuracy but overlook the efficiency in terms of speed and hardware requirements, which has practical implications.

Deep learning with deep neural network has achieved surpassing performances for many applications with rich of data and supervised learning [5,[START_REF] Wu | Label-free bioaerosol sensing using mobile microscopy and deep learning[END_REF][START_REF] Meng | Large-scale multi-class image-based cell classification with deep learning[END_REF][START_REF] Gӧrӧcs | A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples[END_REF][START_REF] Zhang | Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning[END_REF][START_REF] Kim | Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells[END_REF].

However, state-of-art deep neural networks still met challenges in practical applications such as the early warning system [START_REF] Jo | Holographic deep learning for rapid optical screening of anthrax spores[END_REF]. In that applications, the number of targets bioparticles are extremely small, and the number of background images are extremely large. Therefore, it requires the models to have a performance with a low false alarm as well as a high recover rate. Moreover, the target bioparticles are difficult to be collected for building representant training dataset. For example, the appearance of collected images of the bioparticles in the testing environments may be different from them in the training dataset in the particle applications. Those introduce difficulties to recognize them with traditional deep neural networks for the state-of-art deep neural networks assumed a static, closed world and cannot work well on open-set problem [START_REF] Masana | Metric learning for novelty and anomaly detection[END_REF].

Some imaging systems offer an image-based size measurement as well as highthroughput bioparticle analysis [START_REF] Barteneva | [END_REF]. Information-rich micrographs of individual particles are acquired at a high speed, and the particle size is determined by converting the pixel to length at a fixed conversion ratio that is calculated theoretically based on the specification of the optical components, called a calibration. For example, a single pixel corresponds to 0.33 µm in Amnis® ImageStream®X Mk II imaging flow cytometer [START_REF] Erdbrugger | Detection of Extracellular Vesicles Using the ImageStream® X MKII Imaging Flow Cytometer[END_REF] with a 60´ objective, and 0.

Objectives

The major objective of this doctorate thesis is to develop machine learning algorithms for detecting and sizing of microscale bioparticle by using machine learning To enable high accuracy bioparticle size measurement, an bioparticle sizing algorithm pipeline is developed by employing computer vision and machine learning.

The algorithm automatically segments particles from the 2D micrographs, estimates the pixel size of the particles, and predicts the actual size from the pixel information by using a machine learning model from mass training data. Compared to the conventional approaches, the results show that our intelligent pipeline offers more accurate particle sizing by learning from the mass calibration data. This will greatly extend conventional particle sizing and has great potential in the field of environmental monitoring, biomedical diagnostical, and material characterization, etc.

Major Contributions

The major contributions of this PhD thesis are summarized as in the follows: 3) An intelligent pipelined for bioparticle sizing by employing computer vision and machine learning for accuracy sizing is built up and the intelligent pipeline automatically identifies and segments particles from the micrographs, measures the pixel size of the particles, and converts the pixel into actual size using a machine learning model learned from large amounts of training data.

Compared to conventional approaches, the particle size determined by the machine learning model only has a mean percentage error of 4.2% which is five times better than the methods using a fixed pixel-to-size conversion ratio (23.3%). This method leverages the use of different intelligent imaging systems such as imaging flow cytometry for high accurate particle sizing and promises great potential in a wide range of applications in the field of environmental sensing, biomedical diagnostics, and material characterization.

Organization of the Thesis

This thesis is organized with six Chapters. The introduction Chapter of this thesis covers the motivation, objective, major contribution and organization of this thesis as presented in this Chapter. The motivation section shows how this PhD research is important and why it was carried out. The objective section states the main focus of this thesis. The contribution section lists the important findings and innovations in both technological and theoretical point of view.

In Chapter two, a literature survey on current machine learning technologies in bioparticle imaging analysing is introduced. This Chapter provides an overview of basic knowledge of intelligent imaging flow cytometry, the evolution of machine learning and the typical applications, and how machine learning can be applied for assisted intelligent imaging flow cytometry. The future perspectives of machine learning in intelligent imaging flow cytometry are also discussed.

In Chapter three, a deep learning-enabled high-throughput system for predicting bioparticle is reported. This system combines imaging flow cytometry and an efficient artificial neural network called MCellNet. In order to evaluated the bioparticle classification models, a bioparticle images dataset is built up. This Chapter provides an overview of basic knowledge of intelligent imaging flow cytometry, the evolution of machine learning and the typical applications, and how machine learning can be applied for assisted intelligent imaging flow cytometry.

Future perspectives of machine learning in intelligent imaging flow cytometry is also discussed. Imaging flow cytometry is an analytical tool extensively used to detect, sort, count and measure phytoplankton, cells, and other bioparticles [START_REF] Adan | Flow cytometry: basic principles and applications[END_REF][START_REF] Barteneva | [END_REF][START_REF] Basiji | Cellular image analysis and imaging by flow cytometry[END_REF][START_REF] Bonner | Fluorescence activated cell sorting[END_REF]. By combining high-throughput flow cytometry with various imaging acquisition technologies such as multispectral imaging [START_REF] Han | Imaging technologies for flow cytometry[END_REF], imaging flow cytometry is capable of capturing thousands even millions of images with multiparametric morphology information, allowing automated high-throughput data collection. However, human expertise is often required for performing image analysis on traditional imaging flow cytometry.

Chapter 2 Literature Survey

Introduction

Intelligent Imaging Flow Cytometry (IIFC) as shown in Figure 2-1 environmental monitoring [START_REF] Wang | Past, present and future applications of flow cytometry in aquatic microbiology[END_REF], precision agriculture [START_REF] Delaat | Determination of ploidy of single plants and plant populations by flow cytometry[END_REF], and other potential biosensing applications [START_REF] Adan | Flow cytometry: basic principles and applications[END_REF][START_REF] Zucker | Detection of TiO2 nanoparticles in cells by flow cytometry[END_REF][START_REF] Shapiro | Practical flow cytometry[END_REF][START_REF] Mckinnon | Flow cytometry: an overview[END_REF][START_REF] Macey | Flow cytometry[END_REF].

In this Chapter, we focus on recent development in IIFC from the perspective of imaging technologies, the evolution of machine learning for computer vision, and machine learning techniques that are developed specifically for IIFC. The emergent imaging technologies such as multispectral imaging, multi-field-of-view imaging, and serial time-encoded amplified microscopy (STEAM) are discussed to reveal more distinctive features in the bioparticle images. To understand machine learning in imaging flow cytometry, we introduce the fundamentals of visual understanding, the evolution and knowledge of machine learning, increasing the understanding of machine learning in visual perception. Next, we review the most interesting applications of machine learning in this field. Finally, we summarize the review and give the perspectives of future development of machine learning assisted IIFC.

Machine Learning

Machine Vision and Image Analysis

Imaging flow cytometry technologies enable capturing and analysing the images of single bioparticles in high quality and high throughput. Besides the challenges in image acquisition, storage and processing, image analysis also requires significant efforts in the development of IIFC, which promotes the advancements in machine vision.

The working principle of machine vision system [START_REF] Batchelor | Machine Vision Handbook[END_REF] is elaborated here. First, an object is converted into an image signal through a machine vision device such as a camera. Then, the image signal is sent to a dedicated image processing system to obtain the morphological information of the captured object. According to the pixel brightness, color and spatial distribution, the imaging system performs various algorithms on those signals to extract the characteristics of the target object. Next, a control operation of the equipment is generated according to the result of the discrimination algorithms. The goal of computer vision is to fully understand the image of the electromagnetic wave formed by the reflection of the object surface, mainly the visible and infrared parts, because this process is based on optical physics and solid-state physics, which can represent the real world.

Traditional Machine Learning

Since that time, a theoretical framework for object recognition was conceptualized, as well as several general vision theoretical frameworks, visual integration theoretical frameworks based on perceptual feature groups, and many other new research methods and theories have emerged. Consequently, the processing of general 2D

information and the research on the model and algorithm of 3D images has greatly improved and machine vision has developed vigorously with emerging new concepts and theories.

Before the era of deep learning used in machine vision, image analysis methods could be roughly divided into the following five categories: image perception, image pre-processing, feature extraction, inference prediction, and recognition [START_REF] Nixon | Feature extraction and image processing for computer vision[END_REF]. In the early stage of machine learning, among the dominant statistical machine learning groups, little attention was paid to features. The design feature is to combine these pixel values of the image in a statistical or non-statistical form to express the part or whole object that one wants to identify or detect.

As an illustration we show a face-recognition approach using in real-time using

Haar-like features to locate a face as shown in Figure 2-2 [6]. The Viola / Jones facial The first feature measures the difference in intensity between the region of the eyes and a region across the upper cheeks. The feature capitalizes on the observation that the eye region is often darker than the cheeks. The second feature compares the intensities in the eye regions to the intensity across the bridge of the nose. Reproduced with permission from IEEE [6].

detector is a powerful binary classifier consisting of several weak classifiers and is still widely studied today. However, it is time-consuming in the learning phase because adaptive boosting (Adaboost) is used to train the cascade of weak classifiers, such as finding the object of interest (e.g., face). The model needs to split the input image into multiple rectangular blocks and then submit them to the cascaded weak detectors. If the patch passes through all stages of the cascaded weak detectors, it is classified as a positive example. Otherwise, the algorithm will reject it immediately. treats the simulated potential object center as a potential variable. DPM excels at object detection tasks (using bounding boxes for localizing objects) and defeating template matching as compared to other object detection methods that were popular at that time whereby the Histogram of oriented Gradient (HoG) [START_REF] Dalal | Histograms of oriented gradients for human detection[END_REF] feature was used to generate the corresponding "filter" for various objects. The HoG filter can record the edge and contour information of the object and use to filter at various positions in different pictures. When the output response value exceeds a certain threshold, the filter and the object in the picture are treated as highly matched, thus completing the detection of the object.

HoG is a good feature descriptor that has been successfully deployed in human detection problem [START_REF] Satpathy | Human detection by quadratic classification on subspace of extended histogram of gradients[END_REF]. HoG has an advantage on densely capturing the gradient information of images, which is similar to SIFT [START_REF] Lowe | Distinctive image features from scale-invariant keypoints[END_REF], but it demands fewer computation resources. HoG is also resistant to the lighting conditions, e.g., reduces shadows influence and other illumination variations such as smaller rotation and HoG calculates on small bioparticles in a window of 8 × 8 pixels. In that window, the direction of gradient and magnitude are calculated by

𝐺 = 0𝐺 ! " + 𝐺 # " , 𝜃 = 𝑎𝑟𝑐𝑡𝑎𝑛 $ ! $ "
(2-1a)

𝐺 ! = 𝐻 ! * 𝐼(𝑥, 𝑦) , 𝐺 # = 𝐻 # * 𝐼(𝑥, 𝑦) (2-1b)
where I(𝑥, 𝑦) is the input, 𝐻 ! is the vector [ -1 0 1 ] and 𝐻 # is the vector J -1 0 1 K.

Finally Boosting (GB) [START_REF] Natekin | Gradient boosting machines, a tutorial[END_REF] classifier and a random forest (RF) [START_REF] Biau | A random forest guided tour[END_REF] classifier to recognize the Jurkat cells [START_REF] Hennig | An open-source solution for advanced imaging flow cytometry data analysis using machine learning[END_REF], and the identification of label-free white blood cells using the features generated from CellProfiler and classifiers such as K-Nearest Neighbours (KNN), AdaBoost, GB, RF, and Support Vector Machine (SVM) [START_REF] Nassar | Label-free identification of white blood cells using machine learning[END_REF]. The SVM classifier [START_REF] Scholkopf | Estimating the support of a high-dimensional distribution[END_REF]

𝑑(𝑥, 𝑢) = (𝑥 -𝑢 ' ) & Σ ' -% (𝑥 -𝑢 ' ) (2-3)
The classic problem of machine vision is to determine whether a set of image data contains a specific object, image feature, or motion state. This problem can sometimes be solved automatically by an algorithm, but so far, there is no single method that can be widely used to perform well in varied situations, i.e., identify any object in unpredictable environment. The prior art can only solve well in the recognition of specific targets, such as simple geometric figure recognition [START_REF] Peterson | Shape recognition inputs to figure-ground organization in three-dimensional displays[END_REF], face recognition [START_REF] Turk | Face recognition using eigenfaces[END_REF], printed or handwritten document recognition [START_REF] Greanias | The recognition of handwritten numerals by contour analysis[END_REF], and vehicle recognition [START_REF] Ferryman | A Generic Deformable Model for Vehicle Recognition[END_REF]. Unfortunately, these recognition often needs to have a specific lighting, background, and target posture requirements in a specific environment.

Designing features by hand requires a lot of experience such as a profound understanding of the field and data. It may also requires a lot of debugging. Moreover, machine vision engineers not only need to manually design features, but also design a more suitable classifier algorithm for the problem. Meanwhile, the combination of designing features and choosing a classifier at the same time to achieve the best results is a difficult task, requiring well-trained experts.

Deep Learning

Machine vision systems are developed such that users do not need to manually design features and choose classifiers. It is desirable for machine vision systems to learn features and classifiers simultaneously, which means that when designing a certain model, the input is just a picture and the output is its label. With the rapid development of deep learning, the emergence of convolutional neural networks (CNN) has made this idea possible, and the research of computer vision-based on deep learning has also developed rapidly. LeCun, a pioneer of the connectionist approach to AI proposed the first Convolutional Neural Network in LeNet [2] as shown in with two fully connected layers as final layers. In 2012, a deeper and wider neural network -AlexNet was published, which achieved a breakthrough with proposed 10% higher accuracy than traditional methods in ImageNet LSVRC [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF]. Nowadays, deep learning has been applied to a variety of areas and big progress has been made in those fields, including visual recognition [START_REF] Zhao | Object detection with deep learning: A review[END_REF], speech recognition [START_REF] Noda | Audio-visual speech recognition using deep learning[END_REF], biomedicine [START_REF] Wainberg | Deep learning in biomedicine[END_REF], and natural language processing [START_REF] Young | Recent trends in deep learning based natural language processing[END_REF], etc.

Deep learning methods are well-suited to constructing architectures that can be trained end-to-end from image data to result. This approach reduces manual engineering in the traditional approach as shown in One or several fully-connected layers are normally added to the last layer of a convolutional neural network and acts as a classifier for the final decision. It always takes a vector of 𝑚 input as the input volume and generates 𝑛 output with the function, which is expressed as

𝑌 𝑛 = 𝑊 𝑋 𝑚 + 𝑏 (2-4)
where 𝑚 is the input dimension, which is computed with the weights matrix 𝑊 with matrix multiplication and added to a bias offset 𝑏.

The learning and optimization process is used to generate the optimal values of the trainable parameters such as kernel weights in convolution layers and the weights in dense layers. The parameters are optimized by the backpropagation algorithm, which uses a gradient descent method to optimize the model iteratively by minimizing a loss function (e.g., cross-enthropy loss). The three frequently used GD algorithms are batch gradient descent, stochastic gradient descent, and mini-batch gradient descent. Softmax regression [START_REF] Goodfellow | Deep learning[END_REF] of the classification layer outputs was employed to train the network., which can be written as

𝑦 , = .!/0! # $ 1 ∑ 3456! % $ 7 , 𝑗 = 1, 2, … , 𝑛 (2-5)
During the training, the loss is calculated from the model input with forwarding propagation whereby the loss difference backwards propagates from the output to the input layer to generate the gradient of each layer. The parameters of every layer are updated with that gradient and the parameters of the model are stabilized after the iterative process.

A convolutional neural network is a powerful learning-ability neural network that is widely used by image classification and segmentation. The convolutional neural network is inspired by the natural visual perception mechanism from the human's perception system. The early attempt was the proposed neocognitron system architectures but also optimisation methods (stochastic gradient descent, Nesterov accelerated descent, etc. [START_REF] Ruder | An overview of gradient descent optimization algorithms[END_REF]) with progress mostly with GPUs but also low-power CPUs, and fast, low latency disks such as SSDs brings cost-effective hardware to the world, making deep neural network computation affordable and opens the door for deep learning. In 2010, a GPU neural network was published [START_REF] Ciresan | Deep, big, simple neural nets for handwritten digit recognition[END_REF].

In 2012, AlexNet was published [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF], which is relatively deeper than LeNet's Residual Network (ResNet) [START_REF] He | Deep residual learning for image recognition[END_REF], MobileNetV2 [START_REF] Sandler | Mobilenetv2: Inverted residuals and linear bottlenecks[END_REF], SENet [START_REF] Hu | Squeeze-and-excitation networks[END_REF], and BiT [START_REF] Kolesnikov | Big Transfer (BiT): General Visual Representation Learning Supplementary Material[END_REF], etc.

From the structural point of view, one of the CNN's development direction is focused on increasing the number of layers. As the ILSVRC 2015 champion, ResNet has 20 times more layers than AlexNet and 8 times more layers than VGGNet. By increasing the depth, the network can use the increased nonlinearity to derive the approximate structure of the objective function while yielding a better characterization. However, this also increases the overall complexity of the network and makes the network difficult. It can also easily overfit. As the depth of the network increases, the accuracy of the network should increase simultaneously. Adding layers significantly affects the parameter updating on the gradient propagating from the back to front. Increasing the network depth runs the risk of vanishing gradients, resulting in networks that are hard to optimize. In addition, the optimization problem becomes more difficult when the network becomes deeper, with larger parameter space.

Therefore, simply increasing the network depth will result in higher training error.

For example, the performance of a 56-layer network is not as good as that of the 20layer network. Due to this, ResNet was designed with a residual module that allows us to train deeper networks [START_REF] He | Deep residual learning for image recognition[END_REF].

The core idea of ResNet is to add a direct connection channel to the network, known as identity shortcut connection, which is the idea of Highway Network [START_REF] Srivastava | Highway networks[END_REF].

The network structure of traditional deep learning is a nonlinear transformation that is performed on the input while ResNet allows the original input information to be passed directly to the subsequent layers as shown in Figure 2-11. In Ref [START_REF] He | Deep residual learning for image recognition[END_REF], the authors argued that stacking layers will not degrade the network performance with stacking identity mappings (a layer that does not do anything) upon the current network and the resulting architecture would perform the same. In such a case, the deeper model should not produce a higher training error than its shallower counterparts. They hypothesized that fitting the stacked layers to a residual mapping is easier than fitting them directly to the desired underlaying mapping. Traditional convolutional networks or fully connected networks will have more information loss during information transmission. Consequently, they will also cause gradients to disappear or explode and make deep networks unable to train. ResNet solves this problem to a certain extent as it protects the integrity of the information by directly Most existing deep learning algorithms developed mainly focus on the detection accuracy but overlook the tradeoff between speed and hardware requirements. The use of high-end GPU system makes it possible to train complex and deep neural networks, but it is a major hurdle for mass deployment of these deep learning algorithms to commercial IFC for bioparticle analysis due to its high cost and high-power consumption. Very recently, faster and efficient deep learning models, such as MobileNet [START_REF] Howard | Mobilenets: Efficient convolutional neural networks for mobile vision applications[END_REF], DenseNet [START_REF] Huang | Condensenet: An efficient densenet using learned group convolutions[END_REF], SENet [START_REF] Hu | Squeeze-and-excitation networks[END_REF], and MobileNetV2 [START_REF] Sandler | Mobilenetv2: Inverted residuals and linear bottlenecks[END_REF], After these values pass through the activation function, they have no contribution to the subsequent features. Moreover, the convolution parameters is lost. The author demonstrates that the information is lost when dimension equals to 2 or 3 through the change process of the information of interest in. More important, when the dimension is between 15 and 30, there are significantly more information recovered.

have
Through the study of the activation function, the bottleneck in ResNet has been substantially improved for the problem of not bringing too much information loss when extracting features in low-dimensional space. One of the improvements is to use linear bottleneck (e.g., remove ReLU) to do linear transformation instead of the original nonlinear activation transformation. In addition to the above operations, the author considers that in order to reduce the loss of information, the input feature map can be expanded first, and useful information can be embedded into the highdimensional feature map as much as possible, and then proceed depth separable convolution and ReLU operations.

The Inverted Residual Block (IRB) in the MobileNetV2 includes a 1 × 1 expansion convolutional layer, a depthwise convolution layer, and a 1 × 1 projection.

The depthwise convolution layer and 1 × 1 projection layer are referred as the depthwise separable convolution adopted by Xception [START_REF] Chollet | Xception: Deep Learning with Depthwise Separable Convolutions[END_REF], which consists of the depthwise convolution and follows by a pointwise convolution. The depthwise convolution is represented as

𝑿 m 𝒙,𝒚,𝒛 𝒌 = 𝛿[∑ 𝑭 m 𝒊,𝒋,𝒛 𝒌 • 𝑿 𝒙+𝒊-𝟏,𝒚+𝒋-𝟏,𝒛 𝒌-𝟏 ',, + 𝑏 @ A \ (2-6)
where 𝑭 m 𝒌 is the depthwise filter in which the 𝑧 BC channel in 𝑭 m 𝒌 only calculates with the 𝑧 BC channel of 𝑿 𝒌-𝟏 and produces the feature 𝑿 m 𝒌 in the 𝑧 BC channel. The contrastive loss is used to train Siamese network. For the pair of input (𝒙 𝒊 , 𝒙 𝒋 ), it is a positive pair if 𝒙 𝒊 and 𝒙 𝒋 are semantically similar and negative pair if they are dissimilar. The contrastive loss is expressed as [START_REF] Lu | Deep metric learning for visual understanding: An overview of recent advances[END_REF] 𝐿 tu𝑊 (G) , 𝑏

(G) v G)% I w = x ℎ(𝑑 J [𝒙 𝒊 , 𝒙 𝒋 \ -𝜏 % ) (',,)∈𝒮 + x ℎ(𝜏 " -𝑑 J [𝒙 𝒊 , 𝒙 𝒋 \) (',,)∈𝒟 (2-7)
where h(x) = max (0, x) is the hinge loss function, and 𝜏 % and 𝜏 " are two positive thresholds with 𝜏 % < 𝜏 " , respectively. 𝒮 = {(𝑖, 𝑗)} is the similar pairs and 𝒟 = {(𝑖, 𝑗)} is the dissimilar pairs, and Euclidean distance 𝑑 J between 𝒙 and 𝒚 is expressed as

𝑑 J (𝒙, 𝒚) = ‖𝑓(𝒙) -𝑓(𝒚)‖ " = ƒ [𝑓(𝒙) -𝑓(𝒚)\ & (𝑓(𝒙) -𝑓(𝒚)) ) (2-8)
where 𝒙, 𝒚 ∈ 𝝌. The triplet loss is expressed as [START_REF] Hoffer | Deep metric learning using triplet network[END_REF] 𝐿

(𝑥 M , 𝑥 / , 𝑥 ( ) = max (0, 𝑚 + ‡𝑓(𝑥 M ) -𝑓[𝑥 / \ ‡ " " -‖𝑓(𝑥 M ) -𝑓(𝑥 ( )‖ " " ) (2-9)
where 𝑥 M and 𝑥 / are from same class and 𝑥 ( is from difference class.

Imaging Flow Cytometry

Technologies to obtain images with both high temporal and spatial resolution are critical but challenging [START_REF] Han | Imaging technologies for flow cytometry[END_REF]. The fundamental trade-off in imaging technologies is sensitivity, acquisition speed, and the amount of acquired information. Commonly, there are two types of detectors used for imaging: (1) multi-pixelated imaging devices (camera-based), such as charge-coupled device (CCD) and complementary metaloxide-semiconductor (CMOS) [START_REF] Hain | Comparison of CCD, CMOS and intensified cameras[END_REF], and (2) single-pixel photodetectors, e.g., photomultiplier tube (PMT) and avalanche photodiode (APD) [START_REF] Mcclish | Recent advances of planar silicon APD technology[END_REF]. system and the circuit to ensure that bioparticles flow at a constant speed without rotation. Unfortunately, data transfer between the rows without gain (e.g., electron multiplication) also limits it up to 3,000 bioparticles per second.

To increase the throughput, a multi-field-of-view imaging flow cytometry [START_REF] Schonbrun | Microfabricated multiple field of view imaging flow cytometry[END_REF] was developed as depicted in

Deep Learning Applications

Intelligence Then, the area around the maximum pixel with 𝜎 is cropped from the density map and the equation of 𝜎 is expressed as

σ 2 = 0.5p max (2-10)
This process continues until all gaussian distributions in the density map have be removed. The tracking algorithm uses the Hungarian algorithm to detect the objects in the consecutive images and assigns the object number to the detected Finally, the cells are sorted by a dual-membrane, which receives decisions from the image processor and employs cell push-pull mechanism for sorting. The whole process is operated in an automated and real-time manner.

Another example of a deep learning-enabled portable imaging flow cytometer was developed by Gӧrӧcs et al [START_REF] Gorocs | A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples[END_REF]. This device combined holographic imaging and neural network for the rapid detection of harmful algae in the water. The resolution of the device was limited to 25 𝑢𝑚 and only suitable to identify large organisms. The highly accurate algorithm based on a sophisticated densely connected neural network [START_REF] Huang | Densely connected convolutional networks[END_REF] is shown in Figure 2-25 for high-throughput cancer cell detection in blood [5]. For examples, the conventional convolutional neural network, AlexNet [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF], VGG [START_REF] Simonyan | Very deep convolutional networks for largescale image recognition[END_REF], GoogleNet [START_REF] Szegedy | Going deeper with convolutions[END_REF], ResNet [START_REF] He | Deep residual learning for image recognition[END_REF], DenseNet [START_REF] Huang | Densely connected convolutional networks[END_REF], NasNet [124], and PyramidNet [125], etc. Among these deep learning algorithms, they are optimized to achieve high prediction accuracies and required high computational resources, such as Nvidia GTX 1080Ti, which are power hungry, expensive, and occupy huge footprint when designing IIFC.

Summary

In this Chapter, we presented recent developments in intelligent imaging flow cytometry, such as image acquisition technologies and artificial intelligence. We network was demonstrated on imaging flow cytometry [START_REF] Jo | Holographic deep learning for rapid optical screening of anthrax spores[END_REF]. A deep convolution neural network-enabled image-activated cell sorter was reported with a processing speed of 100 events per second [START_REF] Nitta | Intelligent image-activated cell sorting[END_REF], but it required a complex and expensive hybrid hardware/software data processing system (i.e., a field-programmable gate array (FPGA), three central processing units (CPUs), and a graphics processing unit (GPU))

to ensure a faster computation capability.

With increasingly large image datasets and powerful hardware, several deep learning models such as AlexNet [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF], ResNet [START_REF] He | Deep residual learning for image recognition[END_REF], and DenseNets [START_REF] Huang | Densely connected convolutional networks[END_REF], etc., were reported. They were adopted from the winners of ImageNet [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF]. For example, ResNet was the first place of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2015. And DenseNet showed a more advanced performance comparing to ResNet. Those deep learning models were also introduced to IFC for detection and classification tasks [4,5,[START_REF] Huang | Densely connected convolutional networks[END_REF][START_REF] Lumini | Deep learning for plankton and coral classification[END_REF]. They achieved high prediction accuracies of > 95% but required relatively high-cost system, e.g., high-cost GPUs and CPU comparing to an embedded GPU system. For instance, the Intelligent Image-Activated Cell Sorting 2.0 that achieved a high throughput cell sorting with 2000 cells per seconds but Most existing deep learning algorithms developed for IFC mainly focus on the detection accuracy but overlook the trade-off between speed and hardware requirements. High-end GPU system empowers the training of complex deep neural networks, but it is a major hurdle for mass deployment of these deep learning algorithms to commercial IFC for bioparticle analysis due to its high cost and highpower consumption. Very recently, faster and efficient deep learning models, such as MobileNet [START_REF] Howard | Mobilenets: Efficient convolutional neural networks for mobile vision applications[END_REF], SENet [START_REF] Hu | Squeeze-and-excitation networks[END_REF], and MobileNetV2 [START_REF] Sandler | Mobilenetv2: Inverted residuals and linear bottlenecks[END_REF], have attracted great interest from the research community because they are able to achieve comparable classification accuracies using lower-cost embedded hardware. However, they need extensive optimizations for specific tasks. the case study to validate our system. Our testing results show that MCellNet significantly enhances both the accuracy and processing speed, allowing it to be incorporated in drinking water quality inspection. The system also empowers potential high-throughput bioparticle analysis applications in environmental monitoring [START_REF] Wang | Past, present and future applications of flow cytometry in aquatic microbiology[END_REF], clinical diagnostics [START_REF] Pedreira | Overview of clinical flow cytometry data analysis: recent advances and future challenges[END_REF], and other biomedical applications.

a probability vector 𝒚 that indicated the likelihood of the presence of each class. The working principle of MCellNet is as follows. Compared to the traditional convolution, the computational reduction of the depthwise separable convolution is 1/𝑁 + 1/𝐷 A " , where 𝑁 is the number of output channels, and 𝐷 A is the kernel size. Furthermore, the resource efficiency of IRB also increases the resource efficiency with its unique architecture. In addition, the skip connection structure is introduced to IRB, which gives the network an opportunity to access features in earlier stages and lead to a deeper neural network with a high efficiency.

The classification layer is a Fully-Connected layer (FC Layer in Figure 3456). It takes the last output of IRB5 as the input and applies matrix multiplications to the weight matrix 𝑭 to produce the number of output classes (2 or 13). This operation generates class scores to distinguish the targets.

The output equation of this layer can be expressed as

𝑿 𝒐 = 𝑭𝑿 𝒊 + 𝒃 (3-3)
Softmax regression [START_REF] Goodfellow | Deep learning[END_REF] of the classification layer outputs is employed to train the network. The output of Softmax regression 𝒚 can be written as parameter space search over the entire training phase with a learning rate and learning rate decay are also performed. In order to further regularize the neural network, the stride parameters are adjusted on every IRB in addition to the network search. With this modification, the depth and width of MCellNet is shrunken from 120 × 120 to 2 × 2. Furthermore, a

𝑦 , = .!/0! # $ 1 ∑ 3456! % $ 7 & %'( , 𝑗 = 1, 2, … , 𝑛 (3-4)

Training of the Model

Optimization of the Model

Fully-Connected (FC) layer is added to combine the structural features from the underlying features map. The above restructuring leads to MCellNetA with a 99.64% accuracy at a processing speed of 346 images per second, comparing with the best optimized MobileNetV2 in Table 3-2 of the accuracy of 99.51% at the speed of 319 images per second on Nvidia Jetson TX2. Giardia may appear in various shapes depending on its orientation. Microplastic beads appear sphere with different sizes. The naturally existing pollutants appear in various sizes and shapes. Some images appear a bit blur because they were slightly out of focus even though an autofocusing mechanism is applied. For example, the target bioparticle sometime is blur or out of focus as in

Experimental Results and Discussions

Image Acquisition of Bioparticles using Imaging Flow

Evaluation Metrics

MCellNet is capable of rapid and accurate imaging-based detection of In order to evaluate the performance of MCellNet, the accuracy, precision, sensitivity or recall, F1-scores, specificity, and images per second are adopted for both multiclass classification and binary classification and listed in the Table 3-3.

Multiclass Classification

MCellNet multiclass classification model (MCellNetM) shows a comparable performance in terms of average accuracy, precision, recall and F1-scores as compared to MobileNetV2 (Table 34). The individual precision and recall score on multiclass classification are shown in Table 3-5.

The accuracy of MCellNetM reaches 99.69% using the macro average [155].

In terms of the processing speed, MCellNetM is about 37% faster (343 frames per The feature maps generated from the IRB0-5 of MCellNetB in the binary classification task with the t-SNE algorithm were also examined. As shown in Figure 3-14, points in green represent microplastics and natural pollutants, and points in red represent protozoans. The t-SNE graphs from IRB0 to IRB5 (a-f) show that the microplastics and pollutants are well separated even in the shallow layers.

Binary Classification

Summary

In this Chapter, a deep neural network is demonstrated by using imaging flow cytometry as a system for the Cryptosporidium and Giardia detection.

. In those applications, the target bioparticles in the sample are extremely rare with a huge abundant of background particles. For example, the ratio of the target bioparticle and background bioparticles could be 1 in 1000 (0.1%) or even less.

Currently, bioimage analysis has made a huge progress, benefitting from rich-dataset supervised learning using deep neural networks [4,5,[START_REF] Kim | Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells[END_REF]. However, conventional deep neural networks only use simple classifiers such as SoftMax to obtain high accuracy results with the confidence that the deep neural network learns more distinct features than traditional machine learning in classification. Thus, in many practical applications, e.g., rare bioparticle detection, they sometimes get unexpected results [START_REF] Masana | Metric learning for novelty and anomaly detection[END_REF][START_REF] Lu | Deep metric learning for visual understanding: An overview of recent advances[END_REF]159,160]. This is because it is hard to collect representative image data in those applications and the input images in inference time may be distinct from those during training. Those applications also require the model to have a performance of low false alarm as well as high recovery rate in practical environments. For example, a large amount of false alarms will introduce high-cost consequential actions [161].

Up to now, it remains a great challenge in the detection of rare bioparticles in practical applications.

Conventional deep neural networks use simple classifier to make the decision of seen/unseen classes. Therefore, they often make wrong predictions, and do so to an output embedding features in the latent space. Instead of using SoftMax classifier, this approach uses semantic similarity such as the Euclidean distance to constrain the models. It does not rely on cross-entropy loss but proposes another class of network loss, i.e., contrastive loss. Thus, the sum of the output class probabilities is not to be one and this provides it a generatability [START_REF] Masana | Metric learning for novelty and anomaly detection[END_REF]. Generative model is essentially a metric learning problem whereby the key is to learn a large margin distance metric within the latent space when the testing data are usually disjoint from the training dataset.

In this Chapter, a deep neural network model based on deep metric learning for rare bioparticle detection is demonstrated. The model leverages convolutional neural network to study the rich features in the dataset and learning distinct metric by using

Siamese network [168] and contrastive loss, which maximizes the distance of

Model for Rare Bioparticle Detection

The Fundamental of Deep Metric Learning

Unsupervised deep metric learning is used to learn a low-dimensional subspace to preserve useful geometrical information of samples. On the other hand, supervised deep metric learning is used to learn a projection from the sample space to the feature space and measure the Euclidean metric in this feature space to discriminate the results. The metric learning is defined to study a map function 𝑓 with a dataset 𝝌 = {𝒙, 𝒚, 𝒛, … }, whereby 𝑓: 𝝌 → ℝ ( is well defined mapping and 𝑑: ℝ ( × ℝ ( → ℝ + is the Euclidean distance over ℝ ( . Then, 𝑑 J (𝒙, 𝒚) = 𝑑[𝑓(𝒙), 𝑓(𝒚)\ = ‖𝑓(𝒙) -𝑓(𝒚)‖ " is close to zero when 𝒙 and 𝒚 are similar. The mathematical definition of Euclidean distance 𝑑(𝒙, 𝒚) is expressed as [START_REF] Lu | Deep metric learning for visual understanding: An overview of recent advances[END_REF] 𝑑(𝒙, 𝒚) = ‖𝑓(𝒙) -𝑓(𝒚)‖ "

= ƒ [𝑓(𝒙) -𝑓(𝒚)\ & (𝑓(𝒙) -𝑓(𝒚))

) (4-1)
where 𝒙, 𝒚 ∈ 𝝌 and it is assumed that metric 𝑑(𝒙, 𝒚): 𝝌 × 𝝌 → ℝ + satisfies the following properties as

𝑑(𝒙, 𝒚) ≥ 0 (4-2a) 𝑑(𝒙, 𝒚) = 𝑑(𝒚, 𝒙) (4-2b

Deep Metric Learning for Rare Bioparticle Detection

The embedding network structure of deep metric learning model is shown in

𝑿 𝒐 = 𝛿(𝑭𝑿 𝒊 + 𝒃) (4-4)
Parametric ReLU (PReLU) layer is used after the fully-connected layer. The parametric ReLU layer is expressed as

𝑓(𝑥 ' ) = 𝑥 ' , 𝑖𝑓 𝑥 ' > 0 𝑎 ' 𝑥 ' , 𝑖𝑓 𝑥 ' ≤ 0 (4-5)
where 𝑥 ' is the input value and 𝑎 ' is the parameter of the PReLU layer.

Training of Deep Metric Learning

𝐿 tu𝑊 (G) , 𝑏 (G) v G)% I w = x ℎ(𝑑 J [𝒙 𝒊 , 𝒙 𝒋 \ -𝜏 % ) " (',,)∈𝒮 + x ℎ(𝜏 " -𝑑 J [𝒙 𝒊 , 𝒙 𝒋 \) " (',,)∈𝒟 (4-6)
where h(x) = max (0, x) is the hinge loss function, 𝜏 % = 0.9 and 𝜏 " = 1.0 are two positive thresholds with 𝜏 % < 𝜏 " , respectively, and 𝒮 = {(𝑖, 𝑗)} is the similar pairs and 𝒟 = {(𝑖, 𝑗)} is the dissimilar pairs.

The A maximum of 300 epochs is used to train the model. Gaussian distribution [175] is a continuous probability distribution, which has a characteristic with symmetric "Bell curve" shape that quickly falls off toward 0.

Deep Metric Learning based Model

GMM is a probabilistic model, which assumes that the underlying data belong to a linear combination of several gaussian distributions. A GMM model gives a posterior distribution over 𝐾 Gaussian distributions and show better performance on optimize model complexity [176]. The GMM can be represented as [177]

𝑃(𝑥|𝜋, 𝜇, ∑) = x 𝜋 ' 𝒩(𝑥|𝜇 ' , ∑ ' ) P ')% (4-7)
where 𝒩(𝑥|𝜇, ∑) is a normal distribution, 𝑥 is a multidimension vector variable, 𝜇 is the mean of this x and ∑ is the covariance matrix. The 𝒩(𝑥|𝜇, ∑) is given by [177]

𝒩(𝑥|𝜇, ∑) = 1 (2𝜋) E/" |∑| %/" exp (- 1 2 (𝑥 -𝜇) & ∑ -% (𝑥 -𝜇)) (4-8)
where D is the number of dimensions of the feature vector. The 𝜋 ' are mixing coefficients, satisfied 0 ≤ 𝜋 ' ≤ 1 and ∑ 𝜋 ' Expectation-maximization (EM) algorithm is used to find the local maximum likelihood and estimates of individual parameters in GMM (μ and ∑). EM is an iterative algorithm which follows the rule that very iteration strictly increases the maximum likelihood. EM algorithm may not approach to the global optimize point but it can guarantee to local saddle point. The EM algorithm consists two main steps: expectation and maximization.

The expectation step calculates the expectation of the clusters when each 𝑥 ' ∈ 𝑋 is assigned to the clusters with given 𝜇, ∑, π. The maximization step maximizes the expectation in previous step by find suitable parameters.

First, the program randomly assigns samples 𝑋 = {𝑥 % , 𝑥 " , … , 𝑥 ( } to components estimated mean 𝜇̂%, 𝜇̂", …, 𝜇̂A,. For example, 𝜇̂% = 𝑥 S , 𝜇̂" = 𝑥 "R , 𝜇̂T = 𝑥 "% , 𝜇Û = 𝑥 TT , 𝜇V = 𝑥 SR when N = 100, K = 5. And assign

Σ ¦ % = Σ ¦ " = ⋯ = Σ ¦ A = 𝐶𝑜𝑣(𝑥) = 𝐸[(𝑋 -𝑥̅ )(𝑋 -𝑥̅ ) & ]
where 𝑥̅ = 𝐸(𝑋). Last, all the mixing coefficients are set to a uniform distribution with 𝜋 « % = 𝜋 « " = ⋯ 𝜋 « A = % P .

In the expectation step, the 𝑝(

𝐶 A |𝑥 ' , 𝜋 « A , 𝜇Â, Σ ¦ A ) is given by [177] 𝑝[𝐶 A `𝑥' , 𝜋 « A , 𝜇̂A, Σ ¦ A \ = 𝜋 « A 𝒩[𝑥 « ' `𝜇Â, Σ ¦ A \ ∑ 𝜋 « , 𝒩[𝑥 ' `𝜇̂,, Σ ¦ , \ P ,)% (4-10) In the maximization step, [𝜋 « A , 𝜇̂A, Σ ¦ A \ ('+%) = arg max W X $ ,Y X $ ,Z [ $ 𝑝 t𝐶 A -𝑥 ' , [𝜋 « A , 𝜇̂A, Σ ¦ A \ ' w
where each item can be expressed as [177]

𝜋 « A = x 𝑝(𝐶 A |𝑥 ' , 𝜋 « A , 𝜇Â, Σ ¦ A ) 𝑁 D ')% (4-11a) 𝜇Â = ∑ 𝑝(𝐶 A |𝑥 ' , 𝜋 « A , 𝜇Â, Σ ¦ A )𝑥 ' D ')% ∑ 𝑝(𝐶 A |𝑥 ' , 𝜋 « A , 𝜇Â, Σ ¦ A ) D ')% (4-11b) Σ ¦ A = ∑ 𝑝(𝐶 A |𝑥 ' , 𝜋 « A , 𝜇Â, Σ ¦ A )(𝑥 ' -𝜇Â)(𝑥 ' -𝜇Â) & D ')% ∑ 𝑝(𝐶 A |𝑥 ' , 𝜋 « A , 𝜇Â, Σ ¦ A ) D ')% (4-11c)
The whole EM process repeats iteratively until the EM algorithm converges to point From the top to bottom are Cryptosporidium, Giardia, natural pollutants and beads.

All subfigures share the same scale bar. [191,192], and electron microscopy [193].

Bioparticle Classification Evaluation

Sieve analysis [185,194] The Stokes-Einstein equation definition is given by

𝑑 = 𝐾 \ 𝑇 3𝜋𝜂 R 𝐷 (5-1)
where 𝑑 is the hydrodynamic diameter, 𝐾 \ is Boltzmann constant, 𝑇 is the absolute temperature, 𝜂 R is the viscosity of the system, and 𝐷 is the diffusion coefficient. The non-imaging-based sizing techniques mentioned above are unable to accurately determine the size of non-spherical particles due to the limit of applied models. Conventional flow cytometry [START_REF] Adan | Flow cytometry: basic principles and applications[END_REF] determines the size according to the scattering pattern or other optical signatures such as time-of-transition. Before that, the calibration using particles of known size is required. Unfortunately, the calibration is not generalizable because particles of the same size may have substantially different optical signatures due to the difference in materials, surface properties, internal structures, or fluorescent labels. In a 2D sensing case (CCD or CMOS Sensor), the size of individual particle is estimated by converting the pixel to size at a fixed conversion ratio which is determined theoretically according to the specifications of the optical components.

For example, a single pixel in images taken corresponds to 0.33 µm with a 60´ objective, and 0.5 µm with a 40´ objective according to the product specifications.

However, it is noted that this fixed conversion ratio does not always give rise to an accurate particle sizing, probably arising from factors such as the objective error, imaging error, and segmentation error. Hence, the relationship between the pixel number and physical size is difficult to be modelled due to possible nonlinearity.

In this Chapter, the machine learning-based pipeline for imaging-based high accuracy particle sizing is studied. The machine learning-based pipeline automatically segments microparticles from the images, estimates the pixel size of particles, and predicts the physical size from the pixel information using a machine learning model to train there labelled images of calibration spherical beads.

Compared to conventional approaches, our intelligent pipeline offers a more accurate particle sizing by learning from the massive calibration data. This machine learningenabled pipeline would greatly extend the applicability of imaging-based sizing in the field of biomedical diagnostical, environmental sensing, and material characterization. The pipeline algorithm automatically analyzes the pixel information of the target particles and converts the pixel information into actual size based on a machine learning model is shown in Figures 567. First, it generates a contour of the particle using a segmentation algorithm. Then, the contour information is used to estimate the shape of the particle. Finally, the shape information is converted to physical length and width using the pixel-to-size module learnt by a quadratic machine learning model trained with least-squares regression [197] using the spherical beads of known sizes. All the aforementioned operations are integrated into an image processing pipeline to automatically predict the physical size of bioparticles from images acquired using an imaging flow cytometry (Amnis ® ImageStream ® X Mk II [START_REF] Erdbrugger | Detection of Extracellular Vesicles Using the ImageStream® X MKII Imaging Flow Cytometer[END_REF][START_REF] Basiji | Principles of Amnis imaging flow cytometry[END_REF]).

The Model of High Accuracy Bioparticle Sizing

Segmentation and Pixel Measurement

where 𝑔(𝑥, 𝑦) is the output pixel value, 𝑓(𝑥, 𝑦) is the input image pixel, and ℎ(𝑖, 𝑗) is a Gaussian kernel given by [202] The 𝜃 is rounded to 0, 45, 90 or 135 degrees. For example, the 𝜃 in between As a result, the bright regions are expanded, and the individual components with small gaps in between are connected. In contrast, the erode operation uses the minimal value to replace the value in the anchor point to render a thinner bright area. The find contours operation [205] obtains the contour information. A contour is a closed curve where all its points are on the boundary and have the same value. In our algorithm, ellipse is used to approximate the outline of the cells. In the last stage of the imaging processing, the contour information of the cells is passed into an estimator function to obtain the inscribed rotated rectangle of the ellipse.

ℎ(𝑖, 𝑗) = 𝐴𝑒𝑥𝑝 ±-² ('-' * ) ) "] % ) + (,-, * ) ) "] # ) ³´ (

Conversion of Pixel Size to Physical Size

The size converter algorithm converts the pixel to size in micrometers with the machine learning model. The calibration process started with the collecting images of microplastic beads with diameters of 3 𝜇𝑚 , 4 𝜇𝑚 , 4.6 𝜇𝑚 , 5 𝜇𝑚 , 5.64 𝜇𝑚 , (5-5)

The partial derivative with 𝑎 can be calculated by 𝜕𝜀(𝑎, 𝑏, 𝑐) 𝜕𝑎 = x 2(𝑎𝑥 ' " + 𝑏𝑥 ' + 𝑐 -𝑦 ' )𝑥 ' " ( ')%

(5-6a)

Segmentation and Pixel Size Measurement

Physical Size Measurement

The imaging flow cytometer uses a fixed pixel-to-size ratio based on the specifications of the optics for particle sizing. However, this approach often leads to large errors in particle size (Table 5-1). Therefore, a machine learning model is established to determine the pixel-to-size ratio for accurate sizing. Both linear and quadratic regression models are adopted to learn the relationship between the pixel (pixels) and length (𝑢𝑚) of microplastic beads of known sizes. As the RMSE of the quadratic model is smaller than the linear model (0.2657 vs 0.2668), the quadratic curve model was employed. The sizes of the microplastic beads measured using our algorithm and using the fixed pixel-to-size ratio (0.33 𝜇𝑚/pixel with 60´ objective on Amnis Imagestream MKII) are summarized in Table 5- on manufacturer's specifications), the brown dots present the size measured using the fixed pixel-to-size ratio, and the dark green dots represent the size measured using the machine learning model. The sizes of the dots represent the SD of the measurement. The microplastic particles have a narrow distribution with a CV < 2% according to product specifications.

The machine learning model gives rise to significantly more accurate size measurement compared to the approach using fixed pixel-to-size ratio. The sizes of microplastic beads measured using the machine learning model deviate only slightly from the ground truth with a mean percentage error of 4.2% (Table 5-1). In contrast, the mean percentage error using the fixed conversion ratio is 23.3% which is 5 times larger than the machine learning model. These works empower rapid, accurate, and high throughput bioparticle detection and sizing, they promise great potential in a wide range of applications in the field of biomedical diagnostics, environmental monitoring, and material characterization, etc.

As shown in

Recommendations

Recommendations for future work are summarized as follows: network quantification [216-218], knowledge transfer network [219,220], are also valuable to be explored. La cytométrie de flux est l'une des technologies les plus largement adoptées pour l'analyse des bioparticules dans le diagnostic des maladies [START_REF] Van Dongen | Minimal residual disease diagnostics in acute lymphoblastic leukemia: need for sensitive, fast, and standardized technologies[END_REF][START_REF] Kanegane | Flow cytometry-based diagnosis of primary immunodeficiency diseases[END_REF][START_REF] Jennings | Recent advances in flow cytometry: application to the diagnosis of hematologic malignancy[END_REF], l'inspection des aliments [START_REF] Seo | Rapid detection of Escherichia coli O157: H7 using immuno-magnetic flow cytometry in ground beef, apple juice, and milk[END_REF][START_REF] Buzatu | An integrated flow cytometry-based system for real-time, high sensitivity bacterial detection and identification[END_REF] et la surveillance de la qualité de l'eau [START_REF] Gillespie | Assessing microbiological water quality in drinking water distribution systems with disinfectant residual using flow cytometry[END_REF][START_REF] Helmi | Assessment of flow cytometry for microbial water quality monitoring in cooling tower water and oxidizing biocide treatment efficiency[END_REF][START_REF] Safford | Flow cytometry applications in water treatment, distribution, and reuse: A review[END_REF] parmi d'autres applications de détection de bioparticules [START_REF] Adan | Flow cytometry: basic principles and applications[END_REF][START_REF] Zucker | Detection of TiO2 nanoparticles in cells by flow cytometry[END_REF][START_REF] Shapiro | Practical flow cytometry[END_REF][START_REF] Mckinnon | Flow cytometry: an overview[END_REF][START_REF] Macey | Flow cytometry[END_REF]. La cytométrie en flux conventionnelle identifie les bioparticules en fonction des signatures optiques des bioparticules telles que leurs profils fluorescents [START_REF] Adan | Flow cytometry: basic principles and applications[END_REF]. Les bioparticules telles que les cellules sont généralement immuno-marquées avec des marqueurs fluorescents qui ciblent des biomarqueurs spécifiques aux cellules pour faciliter l'analyse. Le marquage fluorescent peut toutefois altérer les propriétés cellulaires et interférer avec les analyses en aval [START_REF] Marrison | Ptychography-a label free, high-contrast imaging technique for live cells using quantitative phase information[END_REF], et aucune connaissance de la morphologie cellulaire n'a pu être dérivée des données rares obtenues à l'aide de la cytométrie en flux conventionnelle.

Network Improving

La morphologie est une caractéristique importante des bioparticules. Les images regorgent d'informations riches sur la morphologie qui pourraient être utilisées pour la détection des bioparticules [START_REF] Helmi | Assessment of flow cytometry for microbial water quality monitoring in cooling tower water and oxidizing biocide treatment efficiency[END_REF][START_REF] Safford | Flow cytometry applications in water treatment, distribution, and reuse: A review[END_REF][START_REF] Adan | Flow cytometry: basic principles and applications[END_REF][START_REF] Zucker | Detection of TiO2 nanoparticles in cells by flow cytometry[END_REF][START_REF] Shapiro | Practical flow cytometry[END_REF][START_REF] Mckinnon | Flow cytometry: an overview[END_REF][START_REF] Macey | Flow cytometry[END_REF][START_REF] Marrison | Ptychography-a label free, high-contrast imaging technique for live cells using quantitative phase information[END_REF]. Les méthodes de référence reposent principalement sur la morphologie des bioparticules avec des techniques d'imagerie basées sur la microscopie pour l'identification microbienne [START_REF] Blasi | Label-free cell cycle analysis for high-throughput imaging flow cytometry[END_REF]. Par exemple, les protozoaires pathogènes dans l'eau potable, Cryptosporidium et Giardia, sont détectés en analysant la morphologie des images microscopiques [START_REF] Kobayashi | Label-free detection of cellular drug responses by highthroughput bright-field imaging and machine learning[END_REF].

Cependant, l'approche d'imagerie traditionnelle basée sur la microscopie nécessite une préparation intensive des échantillons et une analyse manuelle des données, ce qui entrave considérablement son application dans l'analyse de bioparticules à haut débit [START_REF] Wu | Label-free bioaerosol sensing using mobile microscopy and deep learning[END_REF].

La cytométrie en flux basée sur l'imagerie est une technologie d'acquisition d'images à haut débit en couplant des techniques d'imagerie à la cytométrie en flux.

La cytométrie en flux d'imagerie a un débit significativement plus élevé que les techniques d'imagerie traditionnelles basées sur la microscopie et est capable de capturer des milliers à des millions d'images de bioparticules en une seconde en utilisant diverses modalités d'imagerie [START_REF] Han | Imaging technologies for flow cytometry[END_REF]. Les images acquises regorgent d'informations riches sur la morphologie des cellules et des microparticules.

Néanmoins, le grand nombre d'images riches en informations pose un grand défi pour l'analyse des données.

L'apprentissage automatique est souvent utilisé pour identifier les bioparticules telles que les cellules, les planctons et les micro-algues lors de l'analyse d'images [START_REF] Ruske | Machine learning for improved data analysis of biological aerosol using the WIBS[END_REF][START_REF] Rösch | On-line monitoring and identification of bioaerosols[END_REF]. À l'exception de quelques cas où l'apprentissage automatique est utilisé pour la correction, l'amélioration et la reconstruction d'image [START_REF] Gorocs | A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples[END_REF], un certain nombre d'algorithmes d'apprentissage automatique ont été développés pour la détection et la classification des cellules basées sur l'imagerie [5,[START_REF] Wu | Label-free bioaerosol sensing using mobile microscopy and deep learning[END_REF][START_REF] Meng | Large-scale multi-class image-based cell classification with deep learning[END_REF][START_REF] Gӧrӧcs | A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples[END_REF][START_REF] Zhang | Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning[END_REF][START_REF] Kim | Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells[END_REF] avec une précision élevée dans ces travaux, mais ils étaient relativement lents et nécessitaient des ressources de calcul élevées au-delà de la capacité des systèmes embarqués afin de construire une machine de cytométrie en flux d'imagerie abordable. Par exemple, un algorithme très précis basé sur un réseau neuronal sophistiqué et densément connecté [START_REF] Huang | Densely connected convolutional networks[END_REF] pour la détection de cellules cancéreuses à haut débit dans le sang a été rapporté La plupart des algorithmes d'apprentissage automatique développés pour l'imagerie de la cytométrie en flux se concentrent principalement sur la précision de détection, mais négligent l'efficacité en termes de vitesse et d'exigences matérielles, ce qui a des implications pratiques.

L'apprentissage profond avec un réseau neuronal profond a atteint des performances très élevées pour de nombreuses applications riches en données et en apprentissage supervisé [5,[START_REF] Wu | Label-free bioaerosol sensing using mobile microscopy and deep learning[END_REF][START_REF] Meng | Large-scale multi-class image-based cell classification with deep learning[END_REF][START_REF] Gӧrӧcs | A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples[END_REF][START_REF] Zhang | Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning[END_REF][START_REF] Kim | Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells[END_REF]. Cependant, les réseaux neuronaux profonds de pointe rencontrent encore des défis dans des applications pratiques telles que le système d'alerte précoce [START_REF] Jo | Holographic deep learning for rapid optical screening of anthrax spores[END_REF]. Dans ces applications, le nombre de bioparticules cibles

  précision des bioparticules. Le pipeline se compose d'un module de segmentation d'image pour mesurer la taille de pixel de la bioparticule et d'un modèle d'apprentissage automatique pour une conversion précise pixel-taille. L'algorithme a montré une capacité de classification en taille nettement plus précise et présente un grand potentiel pour une large gamme d'applications pour la détermination des tailles de bioparticules. Les méthodes proposées pourraient également être potentiellement appliquées à d'autres analyses de bioparticules à haut débit et en temps réel pour le diagnostic biomédical et la surveillance environnementale. v Mots clés: Apprentissage profond, classification des bioparticules, réseau de neurones à convolution, apprentissage métrique profond, dimensionnement des bioparticules, images de bioparticules x

Figure 1 - 1 : 2 Figure 2 - 1 : 12 Figure 2 - 2 : 15 Figure 2 - 3 : 16 Figure 2 - 4 :

 11221122215231624 Figure 1-1: Categories of Cryptosporidium, Giardia and other viruses and bacteria.2

Figure 2 - 5 : 18 Figure 2 - 6 : 21 Figure 2 - 7 : 22 Figure 2 - 8 :Figure 2 - 9 : 24 Figure 2 - 10 : 26 Figure 2 - 11 : 28 Figure 2 - 12 : 32 Figure 2 - 33 Figure 2 - 14 : 34 Figure 2 - 15 : 36 Figure 2 - 16 : 38 Figure 2 - 17 : 39 Figure 2 - 18 : 40 Figure 2 - 42 Figure 2 - 20 : 43 Figure 2 -Figure 2 - 22 : 45 Figure 2 - 46 Figure 2 - 24 : 47 Figure 2 - 48 Figure 3 - 1 : 57 Figure 3 - 2 : 58 Figure 3 - 3 : 59 Figure 3 - 4 : 60 Figure 3 - 5 : 60 Figure 3 - 6 : 63 Figure 3 - 7 :Figure 3 - 8 : 71 Figure 3 - 9 : 79 Figure 3 - 10 : 80 Figure 3 - 11 : 84 Figure 3 - 12 : 87 Figure 3 - 13 : 89 Figure 3 - 14 : 90 Figure 4 - 1 : 94 Figure 4 - 2 : 95 Figure 4 - 3 : 96 Figure 4 - 4 :Figure 4 - 5 :Figure 4 - 6 :Figure 4 - 7 :Figure 4 - 8 :Figure 4 - 9 :Figure 4 - 10 :Figure 4 - 11 :Figure 4 - 12 :Figure 5 - 1 :Figure 5 - 2 :Figure 5 - 3 :Figure 5 - 4 :Figure 5 - 5 :Figure 5 - 6 :Figure 5 - 7 :Figure 5 - 8 :Figure 5 - 9 :Figure 5 - 10 :Figure 5 - 11 :Figure 5 - 12 :Figure 5 - 13 :Figure 5 - 14 :Figure 5 - 15 :Figure 5 - 16 :Figure 5 - 17 :Figure 5 - 18 :Figure 5 - 19 :

 2518262127222829242102621128212322332143421536216382173921840242220432222452462244724831573258335934603560366337387139793108031184312873138931490419442954396444546474849410411412515253545556575859510511512513514515516517518519 Figure 2-5: Local Binary Patterns. (a) The procedure diagram on local binary patterns

1 :

 1 Summary on machine learning papers with non-commercial IIFC ........ Table 2-2: Summary on machine learning papers with commercial IIFC ................ Table 3-1: Network parameters ................................................................................ Table 3-2: IRBs search for base network .................................................................. Table 3-3: Evaluation metrics ...................................................................................

Figure 1 - 1 :

 11 Figure 1-1: Categories of Cryptosporidium, Giardia and other viruses and bacteria.

  especially deep learning methods. Specifically, three types of areas are explored and discussed, namely: (1) Deep learning-enabled high-speed bioparticle detection; (2) Deep metric learning for rare bioparticle detection; (3) Machine learning-based pipeline for bioparticle sizing. To enable low power and high throughput inference on resource limited embedded devices, for example low cost Nvidia Jetson TX2 [44], a deep learningenabled real-time detection program for high-throughput and label-free bioparticle, such as Cryptosporidium, Giardia, microplastic and other pollutants in the water, is proposed. This intelligent detection platform combines imaging-flow cytometry and an efficient neural network developed known as MCellNet with the optimized target for high processing speed and similar detection accuracy. For dealing with rare bioparticle detection problem, a deep neural network model based on metric learning is proposed. The algorithm leverages convolutional neural network to study the rich features inside training dataset and learns a distinct metric by using Siamese network and contrastive loss which learned to maximize the distance of different classes and minimize the distance of similar classes. The experimental results demonstrate that deep metric learning studies good features and high performance comparing with the traditional deep learning on rare bioparticle detection.

Figure 2 - 1 :

 21 Figure 2-1: Overview of intelligent imaging flow cytometry.

Figure 2 - 2 :

 22 Figure 2-2: The first and second features selected by AdaBoost. The two features are shown in the top row and then overlayed on a typical training face in the bottom row.

Figure 2 - 3 :

 23 Figure 2-3: Detections obtained with a single component person model. The model is defined by a coarse root filter (a), several higher resolution part filters (b) and a spatial model for the location of each part relative to the root (c). The filters specify weights for histogram of oriented gradients features. Their visualization show the positive weights at different orientations. The visualization of the spatial models reflects the "cost" of placing the center of a part at different locations relative to the root. Reproduced with permission from IEEE [7].

Figure 2 - 4 :

 24 Figure 2-4: Working principle of histograms of oriented Gradients.

Figure 2 - 5 :

 25 Figure 2-5: Local Binary Patterns. (a) The procedure diagram on local binary patterns histograms. (b) The diagram on how to calculate Local Binary Patterns.

Figure 2 - 6 Figure 2 - 6 :

 2626 Figure 2-6 in 1989. The input image is a 32 × 32 grayscale image. The first layer

Figure 2 - 7 .Figure 2 - 7 : 8 .Figure 2 - 8 :

 2727828 Figure 2-7: Comparison between (a) traditional handcrafted approach and (b) deep learning.

Figure 2 - 9 :

 29 Figure 2-9: Layers of convolutional neural network. (a) Convolutional operation. (b) Rectified Linear Unit (ReLU). (c) Max-pooling operation.

[ 76 ]

 76 in 1980. By improving the neocognitron, LeCun proposed LeNet-5 to solve the handwritten digits, which established the modern framework of the CNN [2]. LeNet-5 gave a basic idea of CNN such as convolutional neural networks use a three-tier architecture: convolution, down sampling, and nonlinear activation functions. It extracts image space features by using convolution and reduces image average sparsity with down sampling. The activation function takes a hyperbolic tangent or sigmoid function. Multilayer neural network as the final classifier uses sparse connection matrices between layers to avoid large computational costs. LeNet-5 can be trained using the backpropagation algorithm and derive an effective representation of the original image, which allows CNN to recognize the object directly from the original pixels with minimal pre-processing. However, due to the lack of large-scale training data and the computing power was limited, LeNet-5 could not work well on complex problems. From 1998 to 2010, the development of neural networks was intense in the machine-learning community, but not very visible to the computer vision community. The combination of theoretical advances or not only neural

Figure 2 - 10 :Figure 2 - 10 .

 210210 Figure 2-10: ImageNet challenge.

Figure 2 - 11 : 2 - 12 .

 211212 Figure 2-11: Residual learning building block.

  attracted great interest from the research community because they are able to achieve only slightly degraded classification accuracies in return for using lower-cost embedded hardware. Various methods have been proposed to improve network performance with those problems in various aspects. The recent improvements of CNN include the convolutional layer, pooling layer, activation function, loss function, regularization, optimization, and fast computing techniques. MobileNet was proposed by Google LLC in 2017. It is a lightweight convolutional neural network focused on mobile devices and embedded devices. SENet won the 2017 ImageNet LSVRC (ImageNet last session) image classification task championship. Normal convolutional neural network aggregates spatial information and feature dimension (channels) information on a local receptive field in order to obtain global information. Many existing studies have shown the benefits of enhancing spatial information dependence. However, SENet hopes to solve that by learning the importance of different channel characteristics and pay more attention to the dependence between channels. The characteristic of this model is that it proposes a new architecture "Squeeze-and-Excitation" (SE) block to explicitly model the dependencies between channels. Compared with the traditional CNN, it greatly reduces the model parameters and the amount of calculation under the premise of a small decrease in accuracy. DenseNet is proposed in the CVPR2017 best paper "Densely Connected Convolutional Networks". It does not improve network performance from the perspective of increasing the depth and width of the network like the previous network but draws on the idea of ResNet's shortcut connections and establishes dense connections between each layer and all other layers behind. It can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and greatly reduce the number of parameters. However, they need extensive optimizations for specific tasks. MobileNetV2 (Inverted Residuals and Linear Bottlenecks) is an improved version based on the MobileNetV1 model, which has better accuracy and a smaller model than MobileNetV1. MobileNetV2 is characterized by the inverted residualwith linear bottleneck. In the network, features can be further embedded into a lowdimensional subspace through transformation (for example, dimensionality reduction through 1×1 convolution). In addition, MobileNetV1 also reduces the dimensionality of the feature layer through the width factor α, which is a compromise between the amount of calculation and accuracy. However, since the layers of the deep convolutional neural network have a non-linear activation function, reducing the dimensionality of the activation space may cause a lot of information to be lost when using ReLU for activation transformation in a low-dimensional space. Because in the training process, some units of the feature map will inevitably output values below 0.

Figure 2 - 12 :

 212 Figure 2-12: Examples of network architecture for ImageNet. the VGG-19 model is in the left, a plain network with 34 parameter layers is in the middle, and a residual network with 34 parameter layers in the right. The dotted shortcuts increase dimensions. Reproduced with permission from IEEE [81].

Figure 2 -)

 2 Figure 2-13: (a) Depthwise convolution. (b) Pointwise convolution with 256 kernels.

Figure 2 -

 2 Figure 2-14: General pipeline of deep metric learning. It includes embedding network for extracting features to latent embedding space, sampling strategies, and loss functions.

Figure 2 - 15 :

 215 Figure 2-15: Optical systems of typical imaging flow cytometry. (a) Optical configuration of ImageStream imaging flow cytometry. (b) Multiple field-of-view imaging flow cytometer. (c) Schematic illustration of STEAM flow analyzer.Reproduced with permission from Royal Society of Chemistry[START_REF] Han | Imaging technologies for flow cytometry[END_REF].

Figure 2 -

 2 15b. This method projects multiple fields of view into a 2D camera, such as microfabricating multiple parallel microfluidic channels with 𝑁 × 𝑀 micro-lens arrays to capture the image of the 𝑁 × 𝑀 channels simultaneously. Motion blur is a big problem in this kind of imaging cytometry when the targets move too fast and cannot be resolved by the imaging sensor under a fixed exposure time. Temporal coded excitation[START_REF] Gorthi | Fluorescence imaging of flowing cells using a temporally coded excitation[END_REF] is a technique used to avoid motion blur, which employs a pseudo-random code modulated excitation pulse to illuminate the object. A de-blurred algorithm is also applied with a known point spread function to reconstruct the final object. PMT sensors provide superb sensitivity for photon signals with high dynamic range, high bandwidth, and low dark noise, which serve as perfect candidates to implement high-throughput imaging flow cytometry. Normally, a laser scanner is used to generate the images from the time domain signals collected from PMTs such as the ultrafast STEAM [99, 100] as illustrated in Figure 2-15c. STEAM uses a nearinfrared laser light with a wide spectral bandwidth as the illumination. The broadband laser pulses are encoded to 2D with two diffraction grating for scanning and illuminating the bioparticle. The reflected signal is collected and stretched by a dispersive medium. Eventually, the rainbow signal is collected by an APD in series. STEAM can achieve a throughput of 100,000 bioparticles per second. Other examples using PMTs include fluorescence imaging by radiofrequency-tagged emission [101, 102] for high-speed fluorescence imaging, spatial-temporal transformation cytometry, etc. The emerging commercial imaging flow cytometry empowers high-speed bioparticle splitting and microscopic imaging. For example, ImageStreamX Mk II as shown in Figure 2-16 uses high-resolution and high-sensitivity objective lenses to produce brightfield, darkfield, fluorescence, and fluorescence intensity images [96]. It contributes significantly to the advancement of a wide range of quantitative, statistically robust cellular analysis and makes more insight into the following areas such as cellular classification, cellular processes, cell-to-cell interactions, microalgae morphology, population dynamics, etc.

Figure 2 -

 2 Figure 2-16: ImageStreamX Mk II.

Figure 2 -

 2 Figure 2-17: FlowCam System

  Figure 2-18: Submersible Imaging FlowCytobot

Figure 2 -

 2 Figure 2-19: R-MOD (Real-time Moving Object Detector) system. Reproduced with permission from Nature Research [3].

Figure 2 -Figure 2 - 22 :Figure 2 - 24 :

 2222224 Figure 2-21: U-Net. Reproduced with permission from Nature Research [1].

Figure 2 -

 2 Figure 2-25: A classification network based on densely connected neural network.Reproduced from ref.[5] with permission from Nature Research.

1 Figure 3 - 2 .Figure 3 - 2 . 3 - 3 . 3 - 4 .

 132323334 Figure 3-2. The eclipse CG area is further separated to individual Cryptosporidium

Figure 3 - 1 :

 31 Figure 3-1: Collected bioparticles which plotted by length and florescent intensity.

Figure 3 - 2 :

 32 Figure 3-2: The selected CG area (Cryptosporidium and Giardia) with intensity by expert.

Figure 3 - 3 :

 33 Figure 3-3: The fine selected C (Cryptosporidium) area and G (Giardia) area.

Figure 3 - 4 :

 34 Figure 3-4: The histogram graph of the selected Cryptosporidium and Giardia.

Figure 3 - 5 :Figure 3 - 5 .

 3535 Figure 3-5: Overview of MCellNet, a deep neural network assists the imaging flow cytometry (Amnis® ImageStream®X Mk II) in Cryptosporidium and Giardia detection. The system consists of laser, flow cytometry, imaging system, image database and deep neural network.

5 in Figure 3 - 6 .

 536 First, MCellNet consists of one convolutional layer with a filter size of 3 × 3 pixel. Then six IRBs (IRB0-5) is attached to the first convolution layer. Later, the output of the last IRB (IRB5) is flattened. Finally, a Fully-Connected (FC-13/FC-2) layer [136] with 13 output units for multiple classification or 2 output units for binary classification are attached to IRB5's output to generate the class score for bioparticles detection. The first convolutional 2D layer (Block Conv2D in Figure 3-6) [146] takes an ℎ × 𝑤 × 𝑛 input feature map 𝑿 𝒊 , where ℎ is the spatial height, 𝑤 is the spatial width and the 𝑛 is the output channels of the feature map, and it is 120 × 120 × 1 in this case. The input is transformed into a 64 × 64 × 24 output feature maps 𝑿 𝒐 . The input feature map 𝑿 𝐢 is convoluted with a number of feature detectors, each of which is a three-dimensional filter 𝑭 in the present layer, and a bias 𝒃. An activation function 𝛿(𝑥), ReLU in our case, is attached to this convolution operator. The whole layer can be expressed as [86] 𝑿 !,#,@ 𝒐 = 𝛿[∑ 𝑭 𝒊,𝒋,𝒌,𝒛 • 𝑿 𝒙+𝒊-𝟏,𝒚+𝒋-𝟏,𝒌 𝒊 ',,,A + 𝒃 𝒛 \ (3-1) where 𝑧 = 1, 2, …, 𝑚 and 𝑘 = 1, 2, …, n. The six IRBs (IRB0-5) attach to the first convolution layer is shown as IRB0-Each IRB is based on the inverted residual containing a narrow-widenarrow structure. Inside the IRB (sub graph inside the Figure 3-6), there are a 1 × 1expansion convolutional layer, a depthwise convolution layer and a 1 × 1 projection layer. The depthwise convolution and 1 × 1 projection layer are referred to as the depthwise separable convolution. The depthwise separable convolution was adopted by Xception[START_REF] Chollet | Xception: Deep Learning with Depthwise Separable Convolutions[END_REF] which consisted of the depthwise convolution followed by a pointwise convolution. The depthwise convolution can be represented as[START_REF] Howard | Mobilenets: Efficient convolutional neural networks for mobile vision applications[END_REF] 𝑿 m 𝒙,𝒚,𝒛 𝒌 = 𝛿[∑ 𝑭 m 𝒊,𝒋,𝒛 𝒌 • 𝑿 𝒙+𝒊-𝟏,𝒚+𝒋-𝟏,𝒛 𝒌-𝟏 ',, + 𝒃 𝒛 𝒌 \ (3-2) where 𝑭 m 𝒊,𝒋,𝒛 𝒌 is the depthwise filter in which the 𝑧 BC channel in 𝑭 m 𝒊,𝒋,𝒛 𝒌 only calculates with the 𝑧 BC channel of 𝑿 𝒙+𝒊-𝟏,𝒚+𝒋-𝟏,𝒛 𝒌-𝟏 and produces the feature 𝑿 m 𝒙,𝒚,𝒛 𝒌 in the 𝑧 BC channel.

  total number of images by using scikit-learn library[147], respectively. In the task of binary classification, the images of microplastics and natural pollutants were combined into one single class, and the Cryptosporidium and Giardia images were merged into another. The training, validation and test dataset were used to tune the parameters of MCellNet during the training phase, tune the hyper-parameters, and assess the performance of MCellNet, respectively. Deep neural networks are implemented with TensorFlow [148] and are trained over an Ubuntu GPU server [149] with four GPU cards (Nvidia GeForce RTX 2080 Ti) and one Intel CPU (Xeon E5-2650). The weight matrices of the deep neural networks (𝑭 and 𝒃) are initialized with the Glorot uniform initializer [150], and the networks (𝑭 and 𝒃) are trained in an end-to-end fashion using the Adam stochastic optimizing algorithm [151]. The parameters for Adam are 𝛽 % = 0.9, 𝛽 " = 0.999, and a learning rate decay is used for training. A data augmentation mechanism [152] is used in the training process to enhance the training dataset size. The images are augmented by random position transformation, horizontal and vertical flipping, and image rotation and zooming during training. In addition, a class weights [153],

Figure 3 - 7 :Figure 3 - 7 . 3 - 8 .Figure 3 -Figure 3 -Figure 3 -Figure 3 -Figure 3 -

 37373833333 Figure 3-7: Raw image sequence of bioparticles. Each line represents one bioparticle's images. From left to right is fluorescent image, bright-field image, and side scattering image.

Figure 3 - 8 :

 38 Figure 3-8: Bioparticle image dataset. Each row represents one type of bioparticles. From the top to bottom are Cryptosporidium, Giardia, microbeads (12µ𝑚, 4µ𝑚, 5.64µ𝑚, 8µ𝑚, 15µ𝑚, and 3µ𝑚.), and natural pollutants. All sub figures share the same scale bar.

Figure 3 - 10 .

 310 The images are separated by a focus metric -gradient RMS score. The left part is the out of focus images and the right part is the focused images. In certain cases, bioparticles at the 5um edge of the field of view are partially occluded. Moreover, the fluctuation of illumination conditions and vibration will cause variability to the images, which further complicate the detection task.

CryptosporidiumFigure 3 - 9 :Figure 3 - 10 :

 39310 Figure 3-9: Single bioparticle image dataset. Each block represents one type of bioparticle. From the top to bottom are (a) Cryptosporidium, (b) Giardia, microbeads with size of (c) 1.54µ𝑚, (d) 3µ𝑚, (e) 4µ𝑚, (f) 4.6µ𝑚, (g) 5µ𝑚, (h) 5.64µ𝑚, (i) 8µ𝑚, (j) 10µ𝑚, (k) 12µ𝑚, and (l) 15µ𝑚, and (m) natural pollutants. All figures share the same scale bar in (a).

  second) than MobileNetV2 (251 frames per second) in multiclass classification task on Nvidia Jetson TX2 board. The confusion matrices for MobileNetV2 and MCellNetM are shown in Figures 3-11a and b, respectively. In an ideal case with no misclassification, all boxes along the diagonal from the top left to bottom right have a value of 1 and appear white, and all the rest boxes have a value of zero and appear black. With misclassification, the non-diagonal boxes will have non-zero values and appear red. As seen in Figure 3-11, the confusion matrix of MobileNetV2 has significantly more red boxes, indicating higher classification errors. MobileNetV2 has a less satisfactory performance even in the classification of microplastics which are supposedly easier to classify compared to Cryptosporidium and Giardia. This outcome may possibly result from overfitting of MobileNetV2 for it has richer parameters than MCellNetM. In MCellNetM, the microplastics are well separated. In very few occasions, misclassification is observed for microplastics of similar size.For example, a few 3-𝑢𝑚 microbeads are misidentified as 1.54-µ𝑚 and 4-µ𝑚 microbeads. This error possibly results from a few out-of-focus images under existing autofocusing mechanism. In such a scenario, smaller objects may appear bigger.Another possible source of error may come from the natural water pollutants which come in diverse sizes and shapes. Some of the particulate pollutants show image patterns similar to bioparticles, leading to misclassification of pollutants to Cryptosporidium and Giardia.

Figure 3 - 11 :

 311 Figure 3-11: The normalized confusion matrix on multiclass classification task. (a) MobileNetV2 and (b) Our proposed MCellNetM. 13 classes of output include naturally existing pollutants, Cryptosporidium, Giardia, and beads: 1.54 µ𝑚, 3 µ𝑚, 4 µ𝑚, 4.6 µ𝑚, 5 µ𝑚, 5.64 µ𝑚, 8 µ𝑚, 10 µ𝑚, 12 µ𝑚, and 15 µ𝑚. The color grading represents the level of agreement between the prediction and the ground truth with white (1) being full agreement and black (0) being no agreement. In an ideal case with no misclassification, all boxes along the diagonal from the top left to bottom

Figure 3 - 12 :

 312 Figure 3-12: The t-SNE visualization on the IRB0 to IRB5 (a)-(f) of MCellNetM in multiclass classification. Different color is the different target class of Cryptosporidium, Giardia, microbeads (1.54 µ𝑚, 3 µ𝑚, 4 µ𝑚, 4.6 µ𝑚, 5 µ𝑚, 5.64 µ𝑚, 8 µ𝑚, 10 µ𝑚, 12 µ𝑚, and 15 µ𝑚.), and natural pollutants.

Figure 3 - 13 :

 313 Figure 3-13: The False Negative-False Positive rate curve on the binary classification task. The diagonal dashed is the EER. Natural pollutant images are class 0, and all Cryptosporidium and Giardia images are class 1.

Figure 3 - 14 :

 314 Figure 3-14: The t-SNE visualization on the IRB0 to IRB5 (a)-(f) of MCellNetB in binary classification. Natural pollutant images are class 0 (green), and all Cryptosporidium and Giardia images are class 1 (red).

Figure 4 - 1 :

 41 Figure 4-1: Novel examples in the inference time. The conventional deep neural networks often make wrong predictions and do so confidently on some novel examples when the images are not seen in the training dataset. Such as the pollutions are predicted as Giardia or Cryptosporidium with confidence level > 99.99%.

  confidently [162-165]. For example, the conventional deep neural network model sometimes predicts wrongly (It predicts the pollutants as Cryptosporidium or Giardia) with a high confidence level (> 99.99%) as shown in Figure 4-1. These inaccuracies arise from the conventional classification approaches (Figure 4-2), for example, convolutional neural networks (CNNs) use Softmax [160] classifier, which acts as a linear classifier, and limit their ability to detect novel examples [41, 162, 164, 166, 167]. As a result, conventional Softmax based approaches are not suitable for openset rare bioparticle detection. For example, a highly accurate algorithm based on a sophisticated densely connected neural network for bioparticle classification was developed for rare bioparticle detection [5], but it only achieved a sensitivity and specificity of 77.3% and 99.5%, respectively.

Figure 4 - 2 :

 42 Figure 4-2: Conventional deep classification. In deep classification, the model only studies a boundary.

Figure 4 - 3 :

 43 Figure 4-3: Deep metric learning problem. In deep metric learning, the model studies a more generative representation with similar classes are close and the unsimilar classes are far away.

)

  𝑑(𝒙, 𝒛) ≤ 𝑑(𝒙, 𝒚) + 𝑑(𝒚, 𝒛) is widely applied in signature verification [168], face verification and recognition [169], and person re-identification [170].

Figure 4 - 4 .

 44 The input of base network is a grayscale image with 120 × 120 pixels and one convolutional layer with a filter size of 7 × 7 is in the first stage. Then, three residual network blocks (RB0 to RB2)[START_REF] He | Deep residual learning for image recognition[END_REF] are attached to the first convolution neural network layer. The output of the last residual network block RB2 is flattened, and then followed by a fully-connected layer [136] together with a parametric ReLU (PReLU) layer[171]. Finally, a Fully-Connected layer with 2 output units is attached to the PReLU layer to generate the latent feature vector of bioparticles. The detail parameters of the embedding network are listed in Table 4-1.

Figure 4 - 4 :Figure 4 - 5 .Figure 4 - 4 ).

 444544 Figure 4-4: Embedding network structure.

Figure 4 - 5 :

 45 Figure 4-5: Residual blocks in the embedding network.

Figure 4 - 6 :

 46 Figure 4-6: The structure of Siamese network for training deep metric learning. The twin networks share same network parameters. A loss function is attached to this twin network to regularize the network.

  deep metric learning model is implemented with deep learning framework-PyTorch [172] and trained over an Ubuntu GPU server [149] with four Nvidia GeForce RTX 2080 cards as well as the Intel Xeon CPU E5-2650. To train and evaluate the performance of the model, the selected image dataset is randomly split into the training, validation and testing dataset with 48%, 12% and 40% of the total number of images, respectively. Later, images in training dataset are augmented to 10,000 images, and each image is randomly sampled from the dataset and processed by position transformation, horizontal and vertical flipping, rotation or zooming. The weight of the deep neural networks is initialized with the Glorot uniform initializer [150] at a mean value of zero and a standard deviation at 10 -" , and the network is trained in an end-to-end fashion using the Adam stochastic optimizing algorithm [151]. The parameters for Adam are 𝛽 % = 0.9, 𝛽 " = 0.999, and a learning rate decay is used for training. Early stop is also used to prevent overfitting by stopping the training when the model's performance on validation dataset start to degrade [173].

Figure 4 - 7 : 4 - 7 ,

 4747 Figure 4-7: Deep metric learning based classification. The unknown bioparticle is classified to correspond classes. Class label is assigned to classify the unknown particles by the closed cluster center (red). Confidence level is used to present the similarity of unknown particles to certain databased collected by Cryptosporidium and Giardia samples.

D ')R = 1 .

 1 With the assumption that 𝑥 ' are come from independent 𝐾 mixture distributions insider 𝐶 . The equation can be expressed as [177] 𝑃(𝐶|𝜋, 𝜇, ∑) = £ x 𝜋 ' 𝒩(𝑥 ( |𝜇 ' , ∑ ' )

  and gives a maximum likelihood estimate for each 𝜋 « A , 𝜇Â, Σ ¦ A . With the EM process, the parameters of individual Gaussian distributions can be estimated. The studied model can be used to predict the label and confidence of a unknown bioparticle. and CCD camera [142] using a 60 × objective (Figure 4-8a). The raw image sequence files (.RIF) of different samples were captured. The raw bright-field images were extracted from the image sequence files by IDEAS software (accompanying with the ImageStream) and patched to 120 ´ 120 pixels (Figure 4-8b). From millions of raw images acquired, 89,663 images (Figure 4-9) were selected to construct the dataset by experts. The image dataset consists of three classes: Cryptosporidium (2,078 images), Giardia (3,438 images), and other natural pollutants and beads (84,147 images). The bright-field images of bioparticle had complex patterns, such as distinct sizes, degree of aggregation and different internal structures, which complicated the learning task.

Figure 4 - 9 :

 49 Figure 4-9: Bioparticle image dataset. Each row represents one type of bioparticle.

Figure 4 - 10 : 4 - 10 .Figure 4 - 12 .

 410410412 Figure 4-10: Visualization on 2D latent space of traditional deep classification-based model and deep metric learning. (a) traditional deep classification-based model, (b) deep metric learning based model.

Figure 4 - 11 :Figure 4 - 12 :

 411412 Figure 4-11: Visualization on intermediate layers with t-SNE on deep metric learning and traditional classification-based model. (a, c, e) the lower, middle, and high level of deep metric learning, (b, d, f) the lower, middle, and high level of traditional deep classification-based model.

  is a traditional method used to measure the particle size. It utilizes stacked sieves with increasing aperture sizes to clamp particles and generate a size distribution. As shown in Figure 5-1, sieve analysis device is made of four sieves and each sieve has a specific open size. The measured objects are transported by gravity force with vibration, air entrainment or flowing liquid. Each sieve classifies a partial object into bin. When there are 𝑛 sieves, the material can be divided into 𝑛 + 1 particle size bins, and the particle size of each particle size bin is expressed by the corresponding sieve hole size of two adjacent sieves. Other non-imaging-based sizing techniques estimate the particle size indirectly. For instance, static laser scattering [186] measures the gyration size instead of physical one based on the scattering pattern. Dynamic light scattering [187] retrieves the particle size based on the correlation function of scattered light signal, which essentially measures the diffusion coefficient of the bioparticle. For nanoparticle tracking analysis [188], it is based on Brownian motion to obtain the size from the diffusion coefficient of particles by Stokes-Einstein equation. When light irradiates particles smaller than the wavelength, the light scatters in all directions around it. Since laser light is monochromatic and phase coherent, timedependent fluctuations in scattering intensity can be observed from laser light. The fluctuation of the scattering intensity is mainly due to the changes in the relative position of the particles in the solution caused by Brownian motion.

Figure 5 - 1 :

 51 Figure 5-1: Schematic of sieve analysis device that is divided of four sieves, which each sieve reduces open size to classifier the objects into bins.

Figure 5 - 2 :

 52 Figure 5-2: Large particles in dynamic light scattering. Time-dependent fluctuations in scattering intensity can be used to calculate size. Large particles move slow, so that the intensity of scattered light fluctuates slowly.

  Large particles move slowly, and the intensity of scattered light fluctuates slowly as shown in Figure 5-2, while small particles move faster, and the intensity of scattered light fluctuates quickly as shown in Figure 5-3.

Figure 5 - 3 :Figure 5 - 4 , 5 .

 53545 Figure 5-3: Small particle in dynamic light scattering. Time-dependent fluctuations in scattering intensity can be used to calculate size. Small particles move faster, so that the intensity of scattered light fluctuates more quickly.

Figure 5 - 4 :

 54 Figure 5-4: The schematic of the optical system for an imaging flow cytometry. The bioparticles passthrough the imaging centre and each channel records 2D images from a particularly CCD or CMOS camera simultaneously.

Figure 5 - 5 :Figure 5 - 6 :

 5556 Figure 5-5: 2D images of different channel. Each channel recodes 2D image from particularly imaging modality. (a) bright-field image, (b) fluorescent image, (c) side scatter image.

Figure 5 - 7 :

 57 Figure 5-7: Different stage of size measurement pipeline. (a) Bioparticle image. (b) Segmentation. (c) Bioparticle Shape. (d) Bioparticle size in pixel numbers. (e) Bioparticle size in 𝑢𝑚. (a), (b) and (c) share the same scale bar.

Figure 5 - 8 :

 58 Figure 5-8: Different processing stage of segmentation and pixel measurement: (a) Gaussian blur, (b) canny detector, (c) erode, (d) dilate, (e) find contours, and (f) estimate shape. All subfigures are in the same scale bar.

5 - 3 ) 9 .Figure 5 - 9 :

 53959 Figure 5-9: A Gaussian kernel with 𝜎 = 0.25.

Figure 5 - 10 :

 510 Figure 5-10: Horizontal and vertical direction kernels. (a) 𝐺 ! and (b) 𝐺 # .

22. 5

 5 degree to 67.5-degree are mapped to 45-degree. Next, a non-maximum suppression algorithm is applied to remove non-considered pixel so that only the thin lines remain. Finally, a hysteresis stage with high and low threshold is applied on the lines to further improve the results. Dilate and Erode[204] are two basic morphological operations for removing noise, isolating or jointing the individual components, and finding the intensity bumps or holes in an image. The dilate operation uses a kernel, such as 3 × 3 pixels, with an anchor point at the center of the kernel to scan over the image and calculate the maximum pixel value. That maximum value replaces the value in the anchor point.

Figure 5 - 11 : 5 - 12 .

 511512 Figure 5-11: Dilate operation.

7. 32

 32 𝜇𝑚, 8 𝜇𝑚, 10 𝜇𝑚, 12 𝜇𝑚, and 15 𝜇𝑚 (from Thermo Fisher Scientific, Duke Scientific and Polysciences Inc.). Then, they were processed with the segmentation algorithm to generate the beads diameters in pixels. Finally, parameters a, b, and c of the quadratic curve model 𝑦 = 𝑎𝑥 " + 𝑏𝑥 + 𝑐 was learned by minimalizing the error which is expressed as 𝐸𝑟𝑟𝑜𝑟 = 𝜀(𝑎, 𝑏, 𝑐) = x(𝑎𝑥 ' " + 𝑏𝑥 ' + 𝑐 -𝑦 ' )

Figure 5 - 12 :

 512 Figure 5-12: Erode operation.

Figure 5 - 15 :

 515 Figure 5-15: Intersection over Union results of the segmentation algorithm.

Figure 5 -

 5 [START_REF] Safford | Flow cytometry applications in water treatment, distribution, and reuse: A review[END_REF] shows the diameter versus the pixel size of the microplastic beads. The quadratic machine learning regression model is shown as the red curve.

1 and Figure 5 - 17 .

 517 The fixed pixel-to-size conversion ratio is the mainstream approach used by imaging flow cytometry. Our algorithm shows significantly more accurate sizing in comparison.

Figure 5 - 17 shows

 517 the length distribution in both axes of microplastic particles within a distribution range. The red dots represent the actual sizes of beads (ground truth based

Figure 5 - 16 :

 516 Figure 5-16: Quadratic curve-based calibration.

Figure 5 - 17 :

 517 Figure 5-17: The length distribution in both axes of microplastic particles within a distribution range. The circle represents the population distribution with the error in 2𝜎 range. Red color ones are the physical diameters of beads, pink color ones are the beads sizes based on assumption, and cyan color ones are the beads sizes based on machine learning calibration.

Figure 5 - 17 ,

 517 methods using fixed conversion ratio tends to overestimate the size of the particle. In the worst scenario, the percentage error even reaches a value close to 40%. In addition, the SD measured with the machine learning model is also smaller in comparison, which indicates a better precision of particle sizing. The individual measurements of microplastic beads of 3 𝜇𝑚, 5 𝜇𝑚, 12 𝜇𝑚 and 15 𝜇𝑚 using the machine learning model are shown in Figure 5-18.

Figure 5 - 18 :

 518 Figure 5-18: Measurement of individual microplastic particles sizes distribution: (a) 3 𝑢𝑚, (b) 5 𝑢𝑚, (c) 8 𝑢𝑚, (d)10 𝑢𝑚, (e)12 𝑢𝑚, and (f)15 𝑢𝑚.

Figure 5 - 19 :

 519 Figure 5-19: Measurement on Cryptosporidium and Giardia. (a) Length of the Giardia. (b) Width of the Giardia. (c) Diameter of the Cryptosporidium.

6. 2 . 1

 21 Efficient Neural Networks Design Efficient neural networks (DNNs) [208, 209] become critical for embedded applications due to their low storage requirement and computing efficiency. In recent years, the performance of deep neural networks have been greatly improved, and the storage and computing complexity of the network have also increased. How to accelerate and compress deep neural networks and improve the operating efficiency of deep neural networks have become a research hotspot in the field of deep learning. In response to this problem, with the joint efforts of academia and industry, a series of methods have been proposed. Besides the compact network design, other approaches, such as network pruning [210, 211], low-rank decomposition [212-215],

[ 5 ]

 5 . Il avait de bonnes performances de détection, mais sa vitesse était limitée à 100 fps (frames per second), et il devait s'appuyer sur une plate-forme haute performance avec un GPU Nvidia GeForce GTX 1080Ti haut de gamme. Le tri intelligent de cellules activé par l'image 2.0 (Intelligent image-activated cell sorting 2.0) permet d'atteindre un débit plus élevé en triant jusqu'à 2000 événements par seconde, mais il nécessite un serveur de 8 PC avec 8 processeurs multi-coeurs et GPU (NVIDIA GeForce GTX 1080 Ti) pour le traitement d'image avec apprentissage profond[4].

  est extrêmement faible et le nombre d'images d'arrière-plan est extrêmement important. Par conséquent, les modèles doivent avoir une performance avec une faible fausse alarme ainsi qu'un taux de récupération élevé. De plus, les images des bioparticules cibles sont difficiles à collecter pour créer un ensemble de données de référence. Par exemple, l'apparence des images collectées des bioparticules dans les environnements de test peut être différente de celles de l'ensemble de données d'apprentissage. Ces différences introduisent des difficultés pour les reconnaître avec les réseaux de neurones profonds traditionnels étant donné que le recours aux réseaux de neurones profonds suppose un monde statique et fermé et ne peuvent pas bien fonctionner sur un problème à jeu ouvert, de sorte que l'entrée au moment de l'inférence doit appartenir à la même distribution pendant l'entraînement afin d'obtenir de bonnes performances de détection[START_REF] Masana | Metric learning for novelty and anomaly detection[END_REF].Le système d'imagerie offre une mesure de taille basée sur l'image ainsi qu'une analyse de bioparticules à haut débit[START_REF] Barteneva | [END_REF]. Des micrographies riches en informations de particules individuelles sont acquises à une vitesse élevée, et la taille des particules est déterminée en convertissant le pixel en longueur à un taux de conversion fixe qui est calculé théoriquement sur la base de la spécification des composants optiques. Par exemple, un seul pixel correspond à 0,33 µm dans le cytomètre de flux d'imagerie Amnis® ImageStream®X Mk II[START_REF] Erdbrugger | Detection of Extracellular Vesicles Using the ImageStream® X MKII Imaging Flow Cytometer[END_REF] avec un objectif de 60´ et 0,5 µm avec un objectif de 40´ selon les spécifications du fabricant. Cependant, nous avons remarqué que ce taux de conversion fixe ne conduit pas toujours à un dimensionnement précis des particules.Face à ces défis, l'objectif majeur de cette thèse de doctorat est de développer les algorithmes d'apprentissage automatique pour détecter et dimensionner la bioparticule microscopique en utilisant l'apprentissage automatique, en particulier les méthodes d'apprentissage profond. Plus précisément, trois types de domaines sont explorés et discutés, à savoir : (1) détection de bioparticules à grande vitesse activée par l'apprentissage profond ; (2) Apprentissage métrique profond pour la détection de

Figure 1 :

 1 Figure 1: Vue d'ensemble de MCellNet, un réseau de neurones profonds qui permet l'imagerie par cytométrie en flux (Amnis® ImageStream®X Mk II) pour la détection de Cryptosporidium et Giardia. Le système comprend un laser, une cytométrie en flux, un système d'imagerie, une base de données d'images et un réseau neuronal profond.

Figure 2 :

 2 Figure 2: Vue d'ensemble de la configuration du système, un réseau neuronal métrique profond qui permet une cytométrie en flux d'imagerie intelligente (Amnis® ImageStream®X Mk II) pour la détection de bioparticules rares. Le système comprend (a) un système d'imagerie de cytométrie en flux, (b) une base de données d'images, (c) un réseau neuronal métrique profond et (d) un algorithme de décision, etc.

Figure 3 :

 3 Figure 3 : Différentes étapes du pipeline de mesure de taille. (a) Image de bioparticules. (b) Segmentation. (c) Forme des bioparticules. (d) Taille des bioparticules en nombre de pixels. (e) Taille des bioparticules en µm. (a), (b) et (c) partagent la même barre d'échelle.

Table 3 -

 3 4: Average accuracy, precision, recall and F1-score on test dataset ...........

Table 3 -

 3 5: Individual precision and recall score on multiple classification .............

Table 3 -

 3 6: Error rate of different approaches on binary classification .....................

Table 4 -

 4 1: Network parameters of the embedding network .....................................

Table 4 -

 4 2: Estimated parameter of GMM with deep metric learning .....................

Table 4 -

 4 3: Precision, recall and F1-score on test dataset ........................................

Table 4 -

 4 4: Confusion matrix of traditional deep classification ..............................

Table 4 - 5 :

 45 Confusion matrix of deep metric learning-based classification ............

Table 4 - 6 :

 46 Cryptosporidium and Giardia detection using deep metric learning ....

Table 5 -

 5 1: Measurement error analysis ..................................................................

Table 5 -

 5 2: Measurement results on bioparticles .....................................................

	xix

  𝜉 is the margin constant, b is the bias, and 𝐶 ∈ ℛ + is the regularization constant. The 𝜑 function optionally projects the vector of training data into a high dimension feature space ℋ by the so-called "kernel trick", where it can be easier to generate the boundary of decision surfaces. A good choice for 𝜑 is to use Radial Basis Function kernel is used as 𝐾[𝑥 ' , 𝑥 , \ = 𝜑(𝑥 ' ) & 𝜑(𝑥 , ) and 𝐾[𝑥 ' , 𝑥 , \ =

	where the two-class problem (binary problem) was defined as y ∈ {1, -1}. 𝑊 ∈ ℛ 𝒟
	is the weight, exp[-𝛾``𝑥 ' -𝑥 ,	`|" \ , 𝛾 > 0 for the kernels.
	Distance metric such as the Mahalanobis distance classifier is an extension of
	the Least-Square multi-class maximum likelihood classifier taking cross-correlations
	into account [64, 65]. It measures the number of standard deviation distance 𝑑 with
	calculated distance of 𝑥 to a dataset and a mean 𝑢 ' . The covariance matrix is defined
	as the equation ∑ '
	was one of the most popular discriminative classifier before the era of
	deep learning. It translates the vector of training data into a higher dimensional space
	and performs the discrimination. By doing this, the optimal hyper-plane can be
	generated, which splits the dataset into different classes via a training process. In the
	inference phase, it can be used to categorize new examples. SVMs can be expressed
	as the following optimization problem:
	minimize	% "	𝑊 & 𝑊 + 𝐶 ∑ 𝜉 ' ( ')%
	subject to 𝑦 ' (𝑊 & 𝜑(𝑥 ' ) + 𝑏) > 1 -𝜉 '	(2-2)
			𝜉 ' ≥ 0, 𝑖 = 1, … , 𝑛.

-% and T is a standard transpose operation. The result class is predicted by measuring the distance from 𝑥 to classes 𝑖 and assuming the result has the minimal distance from the true predicted class. The Mahalanobis distance can be reduced to the Euclidean distance when the covariance matrix is the identity matrix.

The equation of Mahalanobis distance is expressed as

[START_REF] De Maesschalck | The mahalanobis distance[END_REF]

Table 2 -

 2 1: Summary on machine learning papers with non-commercial IIFC

	Application	Imaging Application Dataset	Algorithm Result	Hardware Referenc
								e
	Cell	Time-	Classify the	Collected CNN model 99 %	Nvidia	[36]
	Classification	stretch	cells. THP1,			accuracy	Tesla K40c
		imaging	MCF7,
			MB231,
			PBMC
	Cell Sorting		Cell sorting Collected CNN model NA	Nvidia	[4]
							GTX-
							1080Ti
	Label-free	Lens-free	Phase-	Collected CNN model NA	NA	[27]
	Analysis of	holographic	contrast color
	Natural Water	imaging	images
	Samples		reconstruction
			and
			identification
			of plankton
	Label-Free	Frequency-	Label-Free	Collected Resnet	accuracy	NA	[25]
	Bioaerosol	division-	Bioaerosol		based	>94%
	Sensing	multiplexed	detection
		(FDM)
		microscope
	Real-time	Time-	Cell sorting	Collected CNN model 95%	Nvidia	[28]
	Inference and	stretch	and cancer			accuracy	Tesla P100
	Cell	imaging	cell				GPU and
	Sorting		classification				Nvidia K80
							GPU
	Image	Lens-less		Collected CNN model		Nvidia	[5]
	Construction	time-					GeForce
	and	resolved					GTX
	Classification	holographic					1080Ti
		speckle					GPU
		imaging
	Label-free	Time-	Drug-treated	Collected linear	accuracy of	NA	[24]
	Detection of	stretch	and -untreated		SVM/CNN	92%
	Cellular	imaging	cells		model
	Drug Responses		classification
	Label-free Cell	Microscopy Label-free	Collected LSBoosting 70.2±2.2%	NA	[23]
	Cycle Analysis		cell-cycle			(G1),
			classification			90.1±1.1%
			of Jurkat cells			(S),
						96.8±0.3%
						(G2) and
						44.0±8.4%
						(M)
	Parasites	Lens-less	Parasitic	Collected CNN model 68-76% (at	Nvidia	[38]
	Detection	time-	detection			the lower	GTX 1080
		resolved				end of our
		holographic				tested
		speckle				concentratio
		imaging				ns) to ~38-
						39% (at the
						higher end
						of our
						tested
						concentratio
						ns)
	Label-free	Microscopy Detection	Collected CNN model 93.3% mAP Nvidia	[3]
	Detection		Beads				GTX 1080
	Reconstructing	Microscopy Cell cycle of	Collected CNN model accuracy of	GPU	[126]
	Cell Cycle and		Jurkat			98.73%
	Disease		cells/diabetic
	Progression		retinopathy

Table 2 -

 2 2: Summary on machine learning papers with commercial IIFC

	Application	Imaging	Application Dataset	Algorithm Result	Hardware Reference
	Imaging Analysis	ImageStrea	Label-free cell-	Collected Gradient	92%	NA	[61]
		m or	cycle		Boosting and	accuracy
		FlowSight	classification		Random
		(Amnis)	of Jurkat		Forest
			cells/segment
			image and
			extract
			features/multic
			lass machine
			learning
	Plankton	NA	Plankton	WHOI,	AlexNet,	F1 <95%	Nvidia	[121]
	Classification		classification	ZooScan,	GoogleNet,		TitanX GPU
				Kaggle	VGG,
					Resnet,
					DenseNet
	Microalgae	FlowCAM Microalgae	Collected CNN model 88.59% of	Nvidia Titan	[120]
	Classification		classification			accuracy	X Pascal
	Plankton and Coral	NA	Automated	3 plankton	AlexNet,	F1 <=95% Nvidia	[122]
	Classification		system for	and 2 coral	GoogleNet,		TitanX GPU
			monitoring	datasets	VGG,
			underwater		Resnet,
			ecosystems		DenseNet,
					MobileNetV
					2, NasNet
	Plankton	NA	Plankton	WHOI	AlexNet,	86.3% of	Nvidia Titan	[123]
	Classification		image		GoogLeNet,	accuracy	X Pascal
			classification		VGG16,		GPU
					ResNet,
					PyramidNet
	Label-Free	ImageStrea	Identification	Collected AdaBoost,	99% of	NA	[62]
	Identification of	m (Amnis)	of white blood		Gradient	accuracy
	White Blood Cells		Cells		Boosting
					(GB), K-
					Nearest
					Neighbors
					(KNN),
					Random
					Forest (RF),
					and Support
					Vector
					Machine
					(SVM)
	Label-Free Leukemia	ImageStrea	Label-free	Collected	linear SVM 98.2%	NA	[119]
	Monitoring	m (Amnis)	Leukemia			accuracy
			monitoring

Table 3 -

 3 1: Network parameters

	Layer/Block	Type	Output dimension	Params
	Conv2d	Convolution	60 × 60 × 24	744
	IRB0	Inverted Residual Block	60 × 60 × 24	760
	IRB1	Inverted Residual Block	30 × 30 × 24	5568
	IRB2	Inverted Residual Block	15 × 15 × 24	9456
	IRB3	Inverted Residual Block	8 × 8 × 24	9456
	IRB4	Inverted Residual Block	4 × 4 × 24	9456
	IRB5	Inverted Residual Block	2 × 2 × 16	8272
	Dense1	Fully Connected	13	845

  for our tasks, and the depth of the neural network is reduced to further decrease the FLOPS. Several models with different number of IRB blocks ranging from 17 to 3 are built and tested (Table3-2).

			The optimized configuration
	has 6 IRBs that shows a comparable accuracy score to the original MobileNetV2 (17
	IRBs) with lesser computation power and with faster processing speed on this dataset.
	Deep learning promises accurate and rapid imaging-based analysis of
	Table 3-2: IRBs search for base network Cryptosporidium, Giardia and microplastics of different sizes. Nevertheless, existing Methods Number of IRBs Images per Second Accuracy
	works in this field rely on computer servers with powerful accelerator and fat neural MobileNetV2 17 258.4 99.4697%
	Model1	10	301.5	99.6225%
	Model2	8	314.7	99.6849%
	MNetSearch	6	319.7	99.5134%
	Model4	3	274	99.4635%
	MCellNet based on IRB with a parameter search for trade-off between accuracy and
	efficiency.
	To find the optimal IRB numbers for the MCellNet, an IRB search is also
	performed. Because our dataset is less complex than ImageNet, a high-level block
	search with the goal to reduce 𝑤 and 𝑑 are conducted on the baseline MobileNetV2

network architecture

[START_REF] Meng | Large-scale multi-class image-based cell classification with deep learning[END_REF]

that outstrips the capability of many mobile and embedded processors for field biomedical applications. It is therefore necessary to develop efficient mobile neural networks with reduced hardware footprint and computing power requirements. Efficient neural network architecture design is a tradeoff between the accuracy and speed by carefully tuning the depth (𝑑), width (𝑤) and resolution (𝑟) of the neural network

[154]

. The target of optimization is to retain a high accuracy while reducing the floating-point operations (FLOPS) which is a key indicator of neural network efficiency. Previous study

[154]

suggests that FLOPS of a neural network is proportional to 𝑑, 𝑤 " and 𝑟 " . A practical approach of optimizing a neural network starts with a handcrafted base neural network block and evolves it through an architecture search. According to this principle, we have designed an architecture for neural network optimization. The input resolution of the neural network is kept constant. The width of the neural network is reduced to search for a suitable network scale

Table 3 -

 3 MCellNetA and MCellNetB) are built for binary classification. MCellNetA is a deep neural network evolved directly from MNetSearch. When training MCellNetA, all images of microplastics are merged with the natural pollutant images into one class (Class 0), and all Cryptosporidium and

		3: Evaluation metrics
	Accuracy		𝑃 + 𝑁 𝑇𝑃 + 𝑇𝑁	(3-5a)
	Precision		𝑇𝑃	(3-5b)
			𝑇𝑃 + 𝐹𝑃
	Sensitivity or recall		𝑇𝑃	(3-5c)
			𝑃
	F1-scores	2 ×	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑟𝑒𝑐𝑎𝑙𝑙	(3-5d)
	Specificity		𝑇𝑁	(3-5e)
			𝑁
	False Negative Rate		𝑃 𝐹𝑁	(3-5f)
	False Positive Rate		𝐹𝑃	(3-5g)
			𝑁
	where TP is true positive, FP is false positive, TN is true negative, FN is false negative,
	P is condition positive and N is condition negative. Particularly, for multiclass
	classification, confusion matrices, also known as error matrix, are used for evaluating
	the performance. Each row of the confusion matrix presents the percentage of
	predicted class, and each column presents the actual class.
	Especially, for the binary classification, the False Negative Rate-False Positive
	Rate (FNR-FPR) curves are used to show the performance and compare the

performance of different approaches at Equal Error Rate (EER), FNR at 0.1% and FPR at 0.1%. EER means the FNR and FPR are equal. Two version of MCellNet binary classification models (

Table 3 -

 3 4: Average accuracy, precision, recall and F1-score on test dataset

					Measurement (%)
		Methods
			Accuracy Precision Recall F1-Score	Images/s
		MCellNetB	99.77	99.53	98.66 99.09	343
	Binary	MCellNetA	99.64	98.71	98.44 98.57	346
	Classification	MNetSearch	99.51	98.08	98.12 98.10	320
		MobileNetV2	99.47	98.15	97.70 97.92	258
	Multiclass	MCellNetM	99.69	99.67	99.41 99.54	343
	Classification	MobileNetV2	99.59	99.39	99.41 99.40	251

Table 3 -

 3 5: Individual precision and recall score on multiple classification

	Class	MCellNetM		MobileNetV2
		Precision	Recall	Precision	Recall
	Pollutants	99.51	99.79	99.67	99.55
	Cryptosporidium	98.14	95.07	95.36	96.51
	1.54 µ𝑚	99.40	99.78	99.85	99.33
	3 µ𝑚	99.78	99.49	99.35	99.13
	4 µ𝑚	99.78	100.0	99.34	99.91
	4.6 µ𝑚	99.78	100.0	99.67	99.78
	5 µ𝑚	100.0	100.0	99.59	99.90
	5.64 µ𝑚	100.0	100.0	99.92	99.92
	8 µ𝑚	99.84	99.84	99.75	99.75
	Giardia	99.56	98.40	99.63	98.84
	10 µ𝑚	99.97	100.0	99.97	99.97
	12 µ𝑚	99.90	100.0	99.95	99.95
	15 µ𝑚	100.0	100.0	100.0	99.82
	Avg	99.67	99.41	99.39	99.41

Table 3 -

 3 6: Error rate of different approaches on binary classification

	Methods	EER (%)	0.1%FNR (%)	0.1%FPR (%)
	MCellNetB	0.63	20.73	2.12
	MCellNetA	0.73	4.19	5.34
	MNetSearch	1.18	11.19	7.89
	MobileNetV2	1.45	39.87	7.29
	MCellNet binary classification model is used to evaluate the performance on two-
	class classification condition. Results indicate that MCellNetA and MCellNetB are
	superior to MobileNetV2 in terms of average accuracy, pression, recall and F1-score
	(

Table 3 -

 3

4). In terms of the processing speed, MCellNetA achieves 346 frames per second and surpasses MobileNetV2 (258 frames per second) by 34% in binary classification task on Nvidia Jetson TX2 board.

Figure 3-13 shows the False Negative Rate-False Positive Rate (FNR-FPR) curve of MCellNetA and MCellNetB compared to MobileNetV2 and MNetSearch. MCellNetA and MCellNetB have significantly better performances with improved error rate compared to MobileNetV2, a well-established deep neural network architecture. The Equal Error Rate (EER), FNR at 𝐹𝑃𝑅 = 0.1% and FPR at 𝐹𝑁𝑅 = 0.1% are summarized in Table

3-6.

	MCellNetB has an error rate of only 0.63% at EER. In comparison, MobileNetV2
	has a relatively large error rate of 1.45%. At the false negative rate of 0.1%,
	MCellNetA has a false-positive rate of 4.19%, whereas it is 39.87% for MobileNetV2.
	At the false positive rate of 0.1%, the false-negative rate of MCellNetB is 2.12%,
	whereas it is 7.29% for MobileNetV2 model.

Table 3 -

 3

4

). This observation suggests that rich labeling may offer more hints for optimization, thereby generating more precise filters for higher accuracies

[158]

. In term of protozoa classification, the MCellNetB achieves a sensitivity of 97.37%, a

MobileNetV2

MNetSearch MCellNetA MCellNetB specificity of 99.95%, a positive predictive value of 99.26% and a negative predictive value of 99.92%.

  Cryptosporidium and Giardia cell line and microplastic beads to demonstrate the capability of the proposed approach. For multiclass and binary classifications, MCellNet achieves accuracies of 99.69% and 99.7%, respectively. Our system is able

	The self-
	developed deep neural network MCellNet adopts the building block (IRB) from
	MobileNetV2 to achieve faster speed and lower power consumption for affordable
	Recently, deep neural networks have shown superb performance in analysing
	machines. We used well-characterized samples, such as commercial
	bioimages for diseases diagnosis and bioparticle classification. Conventional deep
	neural networks use simple classifiers such as SoftMax to obtain high accuracy
	Experimental results show that the deep metric neural network achieved a high
	accuracy of 99.86% in classification, 98.89% in precision rate, 99.16% in recall rate
	and zero false alarm rate. The reported model empowers the imaging flow cytometry
	with capabilities of biomedical diagnosis, environmental monitoring, and other
	could also be potentially applied to other high-throughput single-cell analysis
	biosensing applications.
	applications for environmental monitoring, clinical diagnostics, and other biomedical
	fields.
	93

to detect Cryptosporidium and Giardia with a sensitivity of 97.37% and a specificity of 99.95%. MCellNet has an analysis speed of 346 frames per second outperforming the state-of-the-art deep learning algorithm MobileNetV2 (251 frames per second) in demonstrated tasks. We also notice that even with good training, the machine learning model may still not work well on novelty data that the model did not see before. Deep learning with Bayesian inference may be a good solution. With enough high-quality training data, MCellNet could be extended to detect other types of bioparticles in high speed. The reported system, with its ability to detect and classify Cryptosporidium and Giardia with high speed and accuracy, would empower water monitoring and enable users to determine high-risk bio-contaminants and low-risk microplastics. It results. Therefore, they have limitations in many practical applications which require both low false alarm rate and high recovery, e.g., rare bioparticle detection, in which the representative image data is hard to collect, the training data is imbalance, and the input images in inference time could be different from the training images. Deep metric learning offers a better generatability by using distance information to model the similarity of the images and learning a function maps from images pixels to latent space, playing a vital role in the rare object detection. In this paper, we propose a robust model based on deep metric neural network for rare bioparticle detection.

Table 4 -

 4 1: Network parameters of the embedding network

	Layer/Block	Type	Output dimension	Params
	Conv2d	Convolution	60 × 60 × 64	9536
	RB0	Residual Block	30 × 30 × 64	147,968
	RB1	Residual Block	15 × 15 × 128	525,568
	RB2	Residual Block	8 × 8 × 256	2,099,712
	Pool	Average Pool	2 × 2 × 256	0
	Dense1	Fully Connected	256	262,400
	PReLU	Parametric ReLU	256	1
	Dense2	Fully Connected	2	514

Table 4 -2.

 4

	Table 4-2: Estimated parameter of GMM with deep metric learning
		Mean (𝝁)	Covariances (𝚺)	Coefficients (𝝅)
	Nature			3.9012E-6	-6.2244E-6
		-0.4795	0.8864		0.6390
	Pollutants			-6.2244E-5	3.2312E-5
				9.8237E-5	5.4069E-5
	Cryptosporidium	-0.2034	-0.7312		0.1806
				5.4069E-5	5.5438E-5
				4.3410E-5	2.1701E-5
	Giardia	0.6001	0.5226		0.1804
				2.1701E-5	2.3743E-5

Table 4

 4

	-3. The model based on

Table 4 -

 4 4: Confusion matrix of traditional deep classification

				Prediction
		Class	Nature
				Cryptosporidium Giardia
			Pollutants
		Nature
			29610	35	14
		Pollutants
	Actual	Cryptosporidium	20	807	4
		Giardia	10	8	1357

Table 4 -

 4 5: Confusion matrix of deep metric learning-based classification

				Prediction
		Class	Nature
				Cryptosporidium	Giardia
			Pollutants
		Nature
			29639	17	3
		Pollutants
	Actual	Cryptosporidium	9	820	2
		Giardia	6	9	1360

Table 4 - 6 :

 46 Cryptosporidium and Giardia detection using deep metric learning PReLU) layer with two output units to generate the latent feature vector of bioparticles. For the classification, the GMM model is selected because it can tell us how much confidence it is associated with the target cluster, and it has the same accuracy of 99.86% with the mean center. The model can learn interpretable latent representation that preserves semantic structure of similar and dissimilar images. Well-characterized samples, such as commercial Cryptosporidium

	S/N	Spike Level	Images Number	Manual Counting	Sensitivity Specificity	Alarm	Recovery Rate
		20C	23483	7	85.7%	100%	Yes	85.7%
		20C	18422	8	75.0%	100%	Yes	75.0%
		20C	21834	10	80.0%	100%	Yes	80.0%
		20G	19383	7	100.0%	100%	Yes	100.0%
		20G	18320	9	88.9%	100%	Yes	88.9%
		20G	24872	6	83.3%	100%	Yes	88.3%
		0	20000	0	-	100%	No	-
		0	20000	0	-	100%	No	-
		0	20000	0	-	100%	No	-
		0	20000	0	-	100%	No	-
	Mean				85.5%	100%	-	85.5%

Table 5 -

 5 1: Measurement error analysis

		Learnt model		Fixed ratio (0.33𝝁𝒎/pixel)
	Beads	𝝁	𝝈	Error (%)	𝝁	𝝈	Error (%)
	3.0𝜇𝑚	3.15	0.21	5.1	4.16	0.24	38.8
	4.0𝜇𝑚	4.54	0.73	13.5	5.71	0.82	42.7
	4.6𝜇𝑚	4.28	0.83	7.1	5.42	0.93	17.6
	5.0𝜇𝑚	4.66	0.61	6.9	5.84	0.69	16.8
	5.64𝑢𝑚	5.50	0.46	2.5	6.78	0.51	20.2
	7.32𝜇𝑚	7.29	0.66	0.4	8.80	0.74	20.2
	8.0𝜇𝑚	8.08	0.77	1.0	9.69	0.88	21.2
	10.0𝜇𝑚	10.27	1.11	2.7	12.18	1.27	21.8
	12.0𝜇𝑚	11.75	1.10	2.1	13.89	1.27	15.7
	15.0𝜇𝑚	15.02	1.10	0.1	17.67	1.29	17.8
	Avg.		0.76	4.2		0.86	23.3

Table 5 - 2 .

 52 Our intelligent pipeline determines that the mean height of Giardia oocytes is 11.87 𝜇𝑚 with a SD of 1.9 𝜇𝑚. The mean width of the Giardia oocytes is 7.92 𝜇𝑚 with a SD of 0.75 𝜇𝑚.The Cryptosporidium oocytes are approximately spherical, and the mean diameter Cryptosporidium oocytes measured using our algorithm is 5.03 𝜇𝑚 with a SD of 0.48 𝜇𝑚. In contrast, the mean height and width of Giardia oocytes are 12.94 𝜇𝑚 and 8.45 𝜇𝑚 , and the mean diameter Cryptosporidium oocytes is 5.17 𝜇𝑚 when calculated using the fixed conversion ratio.

Table 5 -

 5 2: Measurement results on bioparticles

		Feature	Mean (𝝁𝒎)	SD (𝝁𝒎)
	Giardia	Height	11.87	1.99
		Width	7.92	0.75
	Cryptosporidium Diameter	5.03	0.48

  Supervised machine learning, especially deep neural network, is domestic today's applications in solving the real problem[221, 222]. Unfortunately, in the traditional machine learning, there exist two main uncertainties: aleatoric uncertainty and epistemic uncertainty. The aleatoric uncertainty is the uncertainty inherent in the data if there are some randomness. The epistemic uncertainty is due to lack of data and cannot estimate the model parameter without any doubt. The model may fail in novel data. Deep learning with metric learning is an important approach for those issues. Bayesian deep neural network is another approach[223]. Bayesian deep neural network can be understood as regularization by introducing uncertainty into the weight of the neural network, which is equivalent to ensemble of infinite groups of neural networks on a certain weight distribution for prediction. For example, in the conventional deep neural network, all the parameters are a value, and the predicted output of the network is also a value. In contrast, all parameters of the Bayesian deep neural network are a distribution, and the predicted output of the network is also a distribution. An conventional deep neural network is to fit the data sample with maximum likelihood estimation (MLE).The difference between Bayesian deep neural network and conventional deep neural network is that its weight parameter is a random variable, rather than a definite value. It is a combination of probabilistic modelling and neural network, and can give the confidence of the prediction result. A priori is used to describe the key parameters and the input of the neural network. Bayesian deep neural network is very critical for many problems, because BNN has the ability to quantify uncertainty. It is very robust and it is a promised approach for further study in bioparticle detection.161. Reichardt, T.A., et al. Analysis of flow-cytometer scattering and fluorescence data to identify particle mixtures. in Optics and Photonics in Global HomelandSecurityIV. 2008. International Society for Optics and Photonics. 162. Bendale, A. and T. Boult. Towards open world recognition. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. 163. Pimentel, M.A., et al., A review of novelty detection. Signal Processing, 2014. 99: p. 215-249. 164. Bendale, A. and T.E. Boult. Towards open set deep networks. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. 165. Hendrycks, D. and K. Gimpel, A baseline for detecting misclassified and outof-distribution examples in neural networks. arXiv preprint arXiv:1610.02136, 2016. 166. Meyer, B.J., B. Harwood, and T. Drummond. Deep metric learning and image classification with nearest neighbour gaussian kernels. in 2018 25th IEEE International Conference on Image Processing (ICIP). 2018. IEEE. 167. Trigueros, D.S., L. Meng, and M. Hartnett, Face recognition: From traditional to deep learning methods. arXiv preprint arXiv:1811.00116, 2018. 168. Bromley, J., et al. Signature verification using a" siamese" time delay neural network. in Advances in Neural Information Processing Systems. 1994. 169. Taigman, Y., et al. Deepface: Closing the gap to human-level performance in face verification. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.

& %'(

Acknowledgements

Acknowledgements ..................................................................................................... i Summary .................................................................................................................... ii

Model for High-speed Bioparticle Detection

Overall Structure of MCellNet

The input of MCellNet is bright-field images of 120 × 120 pixels, and the output is The rare bioparticle detection system is illustrated in Figure 45678. First, samples were spiked and imaged using the imaging flow cytometry (Amnis® ImageStream®X Mk II). Bioparticles such as Cryptosporidium, Giardia, microplastics and other pollutants such as dirt and cell debris with size all from 3 to 14 µ𝑚 that naturally exist in drinking water were included in the study. The naturally existing pollutants were obtained by concentrating 10 liters of drinking water using a water filtration system. Bioparticles were hydrodynamically focused by a sheath flow and flowed through the detection region with PBS used as the sheath medium. Single bioparticles were illuminated with a LED light source, and bright-field images were acquired with a

Experimental Results and Discussions

Profiling of Bioparticles

From the partial derivative and rearranging gives

(5-6b)

The partial derivative with 𝑏 can be calculated by

Base on the partial derivative, the Equation 5-7a can be rearranged as

The partial derivative deal with 𝑐 can be calculated by

From the partial derivative and rearranging gives

(5-8b) By use the linear algebra, the matrix equation regarding the least-square regression parameters of a, b, and c in quadratic curve 𝑦 = 𝑎𝑥 " + 𝑏𝑥 + 𝑐 can be given by

where 𝑠 % , 𝑠 " , 𝑠 T , 𝑠 U , z, 𝛾 #! , and 𝛾 #! ) can be expressed as

(5-10b)

where 𝑥 ' is the pixels size of the individual bead and 𝑦 ' is the corresponded physical size of the bead, and 𝑛 is the total number of beads.

When the linear models are learned, we obtained the parameters m = 0.2905 and b = 0.4785 for the linear model and a = -0.000163, b = 0.301, and c = -0.618 for the quadratic curve model. As the RMSE of the quadratic model is smaller than the linear model (0.2657 vs 0.2668), the quadratic curve model was adopted to implement the size converter module.

Experimental Results and Discussions

Performance Evaluation

To evaluate the performance of the image processing pipeline, the image database of microplastic beads of known sizes and biological cells have been built. where 𝑡𝑎𝑟𝑔𝑒𝑡 is the area in ground truth and 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑜𝑛 is the target segmented area; 𝑦 , is the physical size, 𝑦 « , is the predicted size and 𝑛 is the total number of particles.

The graphic representation of IoU is shown in

Summary

In this Chapter, a machine learning-based pipeline for imaging-based high-accuracy bioparticle sizing is demonstrated. The pipeline consists of an image segmentation module for extracting contours and estimating the pixel size of the bioparticle as well as a machine learning model for accurate pixel-to-size conversion. The computer vision-based segmentation algorithm first resizes the input single-particle images into 120 ´ 120 pixels and removes the noise using a Gaussian blurring. Then, a Canny detector is applied to the processed images to generate the edge images that are subsequently processed with erode and dilating algorithms to generate the output blob images. The segmentation results are evaluated with the IoU score between the contour labelled by human operators and the contour predicted by the algorithm. The image segmentation algorithm achieves 84.4% in the mean IoU. The size converter module converts the pixel to size in micrometers with the machine learning model trained by the collected images of calibration beads. The performance of the machine learning model was evaluated with Root Mean Square Error (RMSE). Final, the particle size determined by the machine learning model only has a mean percentage error of 4.2% which is five times better than the method using a fixed pixel-to-size conversion ratio (23.3%). Our method empowers different intelligent imaging systems such as imaging flow cytometry for high-accurate particle sizing and promises great potential in a wide range of applications in the field of environmental sensing, biomedical diagnostics, and material characterization.

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