Ziheng Yang

Halim Benhabiles

Feryal Windal

Jérôme Follet

Anne-Charlotte Leniere

Keywords: Plasmodium parasite, Cryptosporidium parasite, Deep learning, 2D microscopic image analysis, BIO-MEMS. v vi

In this thesis, we have proposed two main contributions related to the Plasmodium parasite and Cryptosporidium parasite analysis from microscopic images using deep learning techniques.

More specifically, in the first contribution, we have proposed a framework for diagnosing Plasmodium infection in humans using microscopic images of thin blood smears. Compared to the state-of-the-art studies, it is rather based on a straightforward segmentation and classification approaches, permitting the analysis of the parasite itself instead of the cell containing it. In this sense, the framework permits to directly segment the Plasmodium parasite and to distinguish its species among four major classes: P. Falciparum, P. Ovale, P. Malaria and P. Vivax. We demonstrate the efficiency of our framework and notably its potential of generalization over interclass data by exploiting several public datasets. Moreover, we show that our proposed data augmentation technique named Local Parasite Texture Scanning (LPTS) further improves the accuracy of our classification model.

In the second contribution, we have proposed a framework for diagnosing Cryptosporidium infection in dairy cows using fluorescence microscopic images. To this end, we have proposed an original parasite segmentation methodology based on a coarse-to-fine approach which achieves high accuracy on our generated dataset of Cryptosporidium and permits to outperform segmentation methods from the state-of-the-art. We have also proposed a classifier with a high discriminatory power that is used to efficiently distinguish the life stages of the parasites among 4 asexual stages: oocyst, trophozoite, meront, and free form. We show through an experimental study that our classifier achieves high accuracy by analyzing only the parasite itself and without the need of additional information related to the size and the number of nuclei which are required by the biologist to establish the classification.

Résumé

Dans cette thèse, nous avons proposé deux contributions principales liées à l'analyse par apprentissage profond des parasites Plasmodium et Cryptosporidium à partir d'images microscopiques.

Plus spécifiquement, dans la première contribution, nous avons proposé un framework pour diagnostiquer une infection par le parasite Plasmodium chez les humains en utilisant des images microscopiques de frottis sanguins fins. Comparé aux méthodes de l'état de l'art, notre framework est basé sur des approches de segmentation et de classification permettant l'analyse directe du parasite au lieu de la cellule qui le contient. Dans ce sens, le framework permet de segmenter directement le parasite Plasmodium et de distinguer son espèce parmi quatre classes principales : P. Falciparum, P. Ovale, P. Malaria et P. Vivax. Nous démontrons l'efficacité de notre framework et notamment son potentiel de généralisation sur des données interclasses en exploitant plusieurs jeux de données publiques. De plus, nous montrons que la technique d'augmentation des données que nous proposons, appelée Local Parasite Texture Scanning (LPTS), améliore davantage la précision de notre modèle de classification.

Dans la deuxième contribution, nous avons proposé un framework pour diagnostiquer une infection par le parasite Cryptosporidium chez les vaches laitières en utilisant des images microscopiques de fluorescence. À cette fin, nous avons proposé une méthodologie originale de segmentation des parasites basée sur une approche grossière à fine, qui atteint une grande précision sur notre jeu de données généré de Cryptosporidium et qui permet de dépasser en termes de performance les méthodes de segmentation de l'état de l'art. Nous avons également proposé un modèle de classification à haut pouvoir discriminant permettant d'identifier efficacement le stade de vie des parasites parmi 4 stades asexués : oocyste, trophozoïte, méronte, et forme libre. Nous montrons à travers une étude expérimentale que notre modèle atteint une grande précision en analysant uniquement le parasite lui-même et sans avoir besoin d'informations supplémentaires liées à la taille et au nombre de noyaux qui sont nécessaires au biologiste iii afin de réaliser sa classification.

Mots clés : Parasite Plasmodium, Parasite Cryptosporidium, Apprentissage profond, Analyse d'images microscopiques 2D, BIO-MEMS. tracted from [START_REF] Alexander | Past and future trends of Cryptosporidium in vitro research[END_REF]). . . . . . . . . . . . . . . . . . . . . . . 2.5 A sample of each parasite species addressed in [START_REF] Yoselyn | Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification[END_REF]. . . . . 2.6 Microscopic parasites (Toxoplasma, Plasmodium, Babesia) were linked to their morphologically similar macroscopic objects (banana, ring, and pear) (extracted from [START_REF] Li | Parasitologist-level classification of apicomplexan parasites and host cell with deep cycle transfer learning (DCTL)[END_REF]). . . . 2.7 Annotation of leishmaniasis parasite (extracted from [START_REF] Górriz | Leishmaniasis parasite segmentation and classification using deep learning[END_REF]).

3.1 Fully convolutional neural networks (FCNs) [START_REF] Long | Fully convolutional networks for semantic segmentation[END_REF]. . . . . .

Chapter 1 Introduction

Parasitic diseases have a profound impact on global public health. The WHO (World Health Organization) reported in its global Health Estimates of 2020 [START_REF] Dyer | African malaria deaths set to dwarf covid-19 fatalities as pandemic hits control efforts[END_REF] that parasitic and infectious diseases are a major cause of death in Africa. In this thesis, we have been interested in the development of precision analysis tools for two parasites, namely the Plasmodium parasite and the Cryptosporidium parasite. In this context, we will present briefly these two parasites:

• Plasmodium parasite causes the Malaria disease which is an acute febrile human disease. The Malaria is one of the epidemics that possess the potential to threaten human life [START_REF] Alan F Cowman | Malaria: biology and disease[END_REF][START_REF] Wang | Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015[END_REF]. It is widespread in tropical and subtropical areas, particularly in Asia, Sub-Saharan Africa and Latin America [START_REF] Caraballo | Emergency department management of mosquito-borne illness: malaria, dengue, and West Nile virus[END_REF]. The same trend has been observed until now (as shown in Fig 1.1). According to the WHO, in Southeast Asia and Africa regions, the number of mortality due to Malaria has declined from approximately 530,000 (2010) to 380,000 (2018). Nevertheless, since 2016, the decrease in mortality rate has decelerated [START_REF]World malaria report[END_REF]. Therefore, there is a need to develop an automated Malaria diagnostic system that is not only reliable, but can be faster and more economical, especially in resource-limited regions [START_REF] Yang | Deep learning for smartphone-based malaria parasite detection in thick blood smears[END_REF].

• Cryptosporidium parasite causes Cryptosporidiosis which is a diarrheal disease. The parasite may live in the intestine of several mammals. It is important to note that Cryptosporidium is the 5th causative agent of diarrhoeal disease in young children under 5 years of age Chapter 1. Introduction [START_REF] Kotloff | Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, casecontrol study[END_REF]. On farms, parasites are excreted in the external surroundings through the faeces of infected cows as a form called oocyst, which directly contaminates water or food and causes further infections (illustrated in Fig 1 .2) [START_REF] Valeria | Cryptosporidium infection in calves from a rural area of Buenos Aires, Argentina[END_REF][START_REF] Feng | Prevalence and genotypic identification of Cryptosporidium spp., Giardia duodenalis and Enterocytozoon bieneusi in pre-weaned dairy calves in Guangdong, China[END_REF]. Both parasites are generally diagnosed by experts through a visual analysis of microscopic images. These images represent for the Malaria case, blood samples and for the Cryptosporidium case, infection of HCT-8 cell lines with Cryptosporidium parvum. Nevertheless, the manual analysis method does not allow for a transition into large-scale tests. To face this issue, adopting deep learning methods is a natural choice in reason of their high performance demonstrated in medical image analysis [START_REF] Ker | Deep learning applications in medical image analysis[END_REF][START_REF] Litjens | A survey on deep learning in medical image analysis[END_REF][START_REF] Gao | Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview[END_REF]. However, direct exploitation of the existing methods is not sufficient to develop a reliable tool due to the particular visual aspect of these parasites. University of East Anglia5 ) and 3 industrials (Inagro6 , ZLTO 7 , CVE 8 ). The project consists in working on the cryptosporidiosis disease within the cows (caused by Cryptosporidium parasite infection) by reducing its impact in farms. Indeed, the project aims to increase farm productivity in a number of different countries, making these businesses more efficient and successful, with a lower impact on human health. For this purpose, the project aims to provide pilot farms, detection tools and technological devices that can accelerate the discovery of new drugs at the lowest possible cost.

Nevertheless, as it was raised in the beginning of this introduction, we have been also interested in the development of analysis tools of the Malaria parasite. Indeed, we have started our thesis in the midst of the COVID-19 crisis which lasted for almost 2 years and which has involved several lockdowns, leading in our case to a major issue in the process of data acquisition from the partners of the project. Besides, no public Cryptosporidium data were available at that time. For these reasons, we choose to first address the Malaria parasite which belongs to the same group as the Cryptosporidium parasite namely Apicomplexa group and for which several public datasets were already available.

Contributions

In this thesis, we offer two different contributions: (1) the first one is related to the study of the human Malaria diagnosis from microscopic images, and (2) the second one is related to the study of automated Cryptosporidium parasite analysis from fluorescence microscopic images.

Deep learning-based precision tool for Plasmodium parasite analysis -We have proposed a framework for diagnosing Malaria infection in humans using microscopic images of thin blood smears. Compared to the 1.2. Outline 5 state-of-the-art studies, it is rather based on a straightforward segmentation and classification approaches, permitting the analysis of the parasite itself instead of the cell containing it. In this sense, the framework permits to directly segment the Malaria parasite and to distinguish its species among four major classes: P. Falciparum, P. Ovale, P. Malaria and P. Vivax. We demonstrate the efficiency of our framework and notably its potential of generalization over interclass data by exploiting several public datasets. Moreover, we show that our proposed data augmentation technique named Local Parasite Texture Scanning (LPTS) further improves the accuracy of our classification model.

Deep learning-based precision tool for Cryptosporidium parasite analysis -We have proposed a framework for diagnosing Cryptosporidium infection in dairy cows using fluorescence microscopic images. To this end, we have proposed an original parasite segmentation methodology based on a coarse-to-fine approach which achieves high accuracy on our generated dataset of Cryptosporidium and permits to outperform segmentation methods from the state-of-the-art. We have also proposed a classifier with a high discriminatory power that is used to efficiently distinguish the life stages of the parasites among 4 asexual stages: oocyst, trophozoite, meront, and free form. We show through an experimental study that our classifier achieves high accuracy by analyzing only the parasite itself and without the need of additional information related to the size and the number of nuclei which are required by the biologist to establish the classification.

Outline

The manuscript is organized as follows:

• Chapter 2 introduces the state of the art of parasite analysis from microscopic images in a general manner. It is worth mentioning that the Plasmodium parasite studies take up the major part of this • Chapter 4 presents a framework based on deep learning to diagnose the infection of Cryptosporidium parasite in dairy cows. The chapter first points out the limitation of state of the art methods to achieve the goal of accurate parasite segmentation, then it explains how we addressed it. To this end, the chapter discribes our coarse-tofine segmentation approach. It presents also a classification model to distinguish the life stages of the parasite. Finally, this chapter concludes with a set of experiments to demonstrate the efficiency of our framework.

• Chapter 5 summarizes the major results obtained in the frame of this thesis and discusses some research lines to investigate which are mainly on the Cryptosporidium analysis using bright-field images (raw images not including the fluorescence).

Chapter 2

State of the art of parasite image analysis 2.1 Introduction

This chapter introduces the state of the art of parasite analysis from microscopic images in a general manner, including Plasmodium, Cryptosporidium, intestinal parasite, Toxoplasma, Babesia and Leishmania.

Moreover, we also present briefly the life cycle of Cryptosporidium. Our study concerns four morphologies of the asexual phase: oocyst, trophozoite, meront, and free form.

Plasmodium parasite image analysis

In the previous decade, there has been a growing interest regarding the development of microscopic image analysis for use as an aid in Malaria diagnosis [START_REF] Adyasha | Vision-based malaria parasite image analysis: a systematic review[END_REF][START_REF] Poostchi | Image analysis and machine learning for detecting malaria[END_REF][START_REF] Loddo | Recent advances of malaria parasites detection systems based on mathematical morphology[END_REF][START_REF] Zahoor | A review on automated diagnosis of malaria parasite in microscopic blood smears images[END_REF]. Various analysis systems, as proposed in different surveys [START_REF] Adyasha | Vision-based malaria parasite image analysis: a systematic review[END_REF][START_REF] Poostchi | Image analysis and machine learning for detecting malaria[END_REF][START_REF] Loddo | Recent advances of malaria parasites detection systems based on mathematical morphology[END_REF][START_REF] Zahoor | A review on automated diagnosis of malaria parasite in microscopic blood smears images[END_REF], include thick blood smears (a drop of blood over glass slide) and thin blood smears (a drop of blood scattered throughout the slide). In comparison with the two, the system of thick blood smears is mainly used to detection of the parasite presence, while thin blood smears enable the determination of the parasite species. In fact, thin blood smear systems FIGURE 2.1: The life cycles of the Plasmodium parasite (extracted from [START_REF] Nanoti | Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear[END_REF]).

constitute the majority and consist of three main stages: pre-processing of the microscopic image, segmentation of the RBCs (red blood cells) and classification of the segmented cells in the image.

The purpose of the pre-processing is to enhance the image quality by decreasing unwanted noise. In fact, the capture system and its setup influence the rendition of the images directly. With the same blood sample, the illumination, texture and contrast and illumination of the image will vary depending on the used capture protocol, which may impact the discriminative features for segmenting blood cells and detecting the possible infection of parasites in the image. To address this issue, the proposed system leverages different techniques of image filtering for example morphological operations [START_REF] Imran | Automatic detection and classification of malarial parasite[END_REF], contrast stretching [START_REF] Nanoti | Detection of malaria parasite species and life cycle stages using microscopic images of thin blood smear[END_REF] and median filtering [START_REF] Yang | A portable image-based cytometer for rapid malaria detection and quantification[END_REF].

The preprocessed image consists of a population of RBCs, each of which may be infected with the parasite. Hence, current analysis systems have presented segmentation of isolated RBCs as an intermediate stage. In order to achieve this goal, the presented systems have employed a large number of segmentation methods, primarily including conventional methods such as: Watershed segmentation [START_REF] Kumar | Machine learning approach for automated screening of malaria parasite using light microscopic images[END_REF], Gaussian mixture model [START_REF] Abbas | Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears[END_REF] and Otsu binarization [START_REF] Pillai | Convolutional neural networkbased malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner[END_REF]. These methods initially outline the RBC in the image and crop out a set of images where each image represents an isolated cell.

The final stage is performed through the classification of each RBC (cropped image) in order to determine its infection status. At this status, the presented system includes the analysis from a simple binary classification to more complex classifications. To be precise, in the case of binary classification, the system distinguish if the blood cells are infected with parasites [START_REF] Rajaraman | Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images[END_REF][START_REF] Pillai | Convolutional neural networkbased malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner[END_REF][START_REF] Abbas | Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears[END_REF]. Nevertheless, with classification for multiple class, the system is able to recognize the parasite species [START_REF] Peñas | Malaria parasite detection and species identification on thin blood smears using a convolutional neural network[END_REF] and, the life stage of it under certain circumstances [START_REF] Salam Shuleenda | Hybrid classifier based life cycle stages analysis for malariainfected erythrocyte using thin blood smear images[END_REF]. Moreover, recent references [START_REF] Adyasha | Vision-based malaria parasite image analysis: a systematic review[END_REF][START_REF] Poostchi | Image analysis and machine learning for detecting malaria[END_REF][START_REF] Loddo | Recent advances of malaria parasites detection systems based on mathematical morphology[END_REF][START_REF] Zahoor | A review on automated diagnosis of malaria parasite in microscopic blood smears images[END_REF] show that nearly all classification methods proposed use machine learning techniques to characterize parasites using hand-designed characteristics.

Consecutive research efforts on Malaria diagnostic systems based on thin blood smear image analysis [START_REF] Molina | Sequential classification system for recognition of malaria infection using peripheral blood cell images[END_REF][START_REF] Delgado-Ortet | A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection[END_REF][START_REF] Sriporn | Analyzing Malaria Disease Using Effective Deep Learning Approach[END_REF][START_REF] Maity | Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet[END_REF][START_REF] Syed Saiden | Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers[END_REF] suggest that it is still a challenging task and more research is needed to strengthen the robustness of the system.

Molina et al. [START_REF] Molina | Sequential classification system for recognition of malaria infection using peripheral blood cell images[END_REF] proposed a system following the processing pipeline presented previously. Precisely, the system considers only the green component of the microscopic image and applies a set of filters to improve its quality: i) an adaptive histogram equalization to enhance the contrast between RBCs and the background and ii) median and Gaussian filters to reduce the background noise. The resulting image is then binarised using Otsu thresholding and RBCs are segmented and cropped using watershed algorithm. A cascade of 3 classifiers are then used to classify each cropped image into 6 classes: normal RBC, RBC with parasite inclusion and 4 classes covering RBC with other erythrocyte inclusions (non parasite). To train and test the classifiers, authors built their own Malaria image dataset from 87 patients and split it into 4633 RBC images for training and 11027 Chapter 2. State of the art of parasite image analysis of RBC images for testing. To feed the classifiers, each image has been characterized by a set of 2852 handcrafted features including shape, texture and color information. Authors reported a cell segmentation performance of 97.4% without mentioning the used metric and an overall parasite infection classification accuracy of 97.7% using SVM classifiers.

The system proposed by Delgado-Ortet et al. [START_REF] Delgado-Ortet | A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection[END_REF] permits to classify each segmented RBC into two classes namely Malaria infected or not. To this end, authors designed their own CNN model to learn an RGB pixel based cell segmentation. The segmentation model is composed of 3 sequential blocks of convolution layers: i) a down-scaling block for extracting the RBCs pixel features from the RGB input image (population of RBCs), ii) an up-scaling block to align the size and the shape of the extracted information with those of the input image, iii) a classification block to predict the belonging to a RBC or not for each pixel of the input image. The cell segmentation model has been trained on 169 images from 4 patients and validated on 17 images of 1 patient, the overall representing a total of 5911 RBCs. Authors reported a Jaccard coefficient ranged between 0.80 (lowest) and 0.94 (highest) for the 17 tested images. For the classification of each cropped RBC with respect to the parasite infection, authors proposed also their own CNN model which has been trained from scratch and tested using another public Malaria dataset composed of 27 558 cropped RBCs images [START_REF] Rajaraman | Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images[END_REF]. The system reached on the test set (20% of this dataset) a classification accuracy of 95%. However, the classification reached 75.39% of accuracy when tested on the dataset used for training the segmentation model. Authors explained that this drop in performance is due to the difference between the acquisition parameters of the two datasets.

A similar deep learning based binary classifier (infected or not) as in [START_REF] Delgado-Ortet | A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection[END_REF] has been proposed by Sriporn et al. [START_REF] Sriporn | Analyzing Malaria Disease Using Effective Deep Learning Approach[END_REF]. However, authors considered only the classification task and didn't address the segmentation one. To train the classifier, they used the public dataset composed of 27 558 cropped RBCs images [START_REF] Rajaraman | Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images[END_REF] and have conducted a comparative experimental study between 6 popular deep architectures namely AlexNet, VGG-16, NasNetMobile, ResNet-50, Inception-V3 and Xception. To this end they explored different transfer learning strategies together with several optimizers. According to the authors, the best performance in term of classification accuracy has been obtained using Xception architecture. Precisely, they reached an accuracy of 99.28% using Nadam optimizer and Mish activation function. However, authors didn't precise the size of the cropped RBCs test set.

The system proposed by Maity et al. [START_REF] Maity | Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet[END_REF] allows to classify the RBCs into 6 classes corresponding to Normal RBCs, Falciparum species with two life stages and Vivax species with three life stages. To segment the RBCs from the input image, authors proposed to first correct the image color using an automatic equalization color technique then to train a pixel based MLP binary classifier (a Multi Layer Perceptron artificial network). The network is fed with pixels associated feature vectors, each one initially composed of 230 handcrafted descriptors covering geometrical and textural information. Authors exploited the training loss function versus one feature exclusion from the 230 in order to keep the most significant features (top 20). The final trained classifier outputs a pixel probability map of the input image which is post-processed using: i) a graph-cut algorithm to refine the borders of each segmented RBC and ii) a watershed algorithm to separate overlapped RBCs. Authors used for training the cell segmentation model their own dataset composed of 210 images and representing a total of 2720 RBCs. They used as a benchmark another dataset of 38 images (524 cells) and reported a Jaccard coefficient of 0.98 on the resulting segmentation but they didn't indicate how they exploited the data in order to reach this result. To classify the cropped images of RBCs, the authors proposed to train a classifier based on an adapted CapsNet deep architecture. To this end, they gathered the two datasets used for segmentation into one set (3244 RBCs) and augmented it to reach 141600 cropped images of RBCs. According to the authors, the classifier achieved an overall accuracy of 98.7% on the test set which corresponds to 15% of the augmented set.

Chapter 2. State of the art of parasite image analysis

Abbas and Dijkstra [START_REF] Syed Saiden | Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers[END_REF] proposed a system that permits to directly segment the parasite (only the falciparum species) instead of the RBC and classify its life stage. To segment the parasite, the authors designed a pixel based segmentation model using a Random forest binary classifier. To this end, a set of 9 handcrafted features is extracted for each pixel of the input image covering mainly Gaussian blur and average intensities while considering the image channels separately. The training of the segmentation model permits to output a binary mask which is refined by filling the holes of detected parasites. Authors trained and tested the segmentation model on their own publicly shared dataset composed of 837 images of RBCs collected from 16 patients infected with Falciparum. They reported an average Dice coefficient of 0.82 obtained on the whole dataset using a cross validation evaluation method. For the life stage classification of segmented falciparum parasites, authors considered 112 x 3 channels global handcrafted features extracted on the parasite cropped images including intensity and area related features. They trained a random forest classifier to learn 8 classes: 6 parasite life stages, white blood cell and debris. Authors reported an average classification accuracy of 58.8% when classifying all the life stages and 82.7% when simplifying the life stages to the three main ones namely ring, trophozoite and schizont. Some research works have proposed parasite-related analysis approaches from the dataset MP-IDB [START_REF] Di Ruberto | A region proposal approach for cells detection and counting from microscopic blood images[END_REF][START_REF] Di Ruberto | Blob Detection and Deep Learning for Leukemic Blood Image Analysis[END_REF][START_REF] Di Ruberto | Detection of red and white blood cells from microscopic blood images using a region proposal approach[END_REF][START_REF] Maity | Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet[END_REF]. Di Ruberto et al. [START_REF] Di Ruberto | A region proposal approach for cells detection and counting from microscopic blood images[END_REF] proposed a method called Edge Boxes for detecting and quantifying red cells from a microscopic blood image. Di Ruberto et al. [START_REF] Di Ruberto | Blob Detection and Deep Learning for Leukemic Blood Image Analysis[END_REF] proposed an efficient system for detecting white blood cells and classifying them as being affected by leukemia or healthy. Another article proposed by Di Ruberto et al. [START_REF] Di Ruberto | Detection of red and white blood cells from microscopic blood images using a region proposal approach[END_REF] leveraging the dataset MP-IDB to detect and quantitatively analyze red and white blood cells from blood images. Besides, Maity et al. [START_REF] Maity | Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet[END_REF] have also evaluated the feasibility of their algorithm on MP-IDB dataset. After Red Blood Cells (RBCs) segmentation, a trained model Capsule Network is applied in order to classify segmented blood cells to identify species and stages of the Malaria parasites. In particular, only two Malaria species (Falciparum and Vivax) and three stages (Ring, Gametocyte and Schzonat) 2.3. Cryptosporidium parasite image analysis 13 are considered.

Cryptosporidium parasite image analysis

A brief biological introduction -Cryptosporidium is an intracellular parasite that occupies intracellular but extracytoplasmic vesicles in the host [START_REF] Beyer | Cryptosporidium parvum (Coccidia, Apicomplexa): some new ultrastructural observations on its endogenous development[END_REF][START_REF] Petry | Structural analysis of Cryptosporidium parvum[END_REF][START_REF] Steven | The cell biology of Cryptosporidium infection[END_REF]. Its life cycle begins with the asexual stage, followed by the sexual stage [START_REF] Alexander | Past and future trends of Cryptosporidium in vitro research[END_REF]. The detailed information of different life stages is illustrated in Our study concerns four morphologies of the asexual phase: oocyst, trophozoite, meront, and free form. As shown in Fig 2 .4, ingestion of an oocyst leads to a process called excystation, releasing four parasites named free forms [START_REF] David W Reduker | Ultrastructure of Cryptosporidium parvum Oocysts and Excysting Sporozoites as Revealed by High Resolution Scanning Electron Bibliography Microscopy 1[END_REF][START_REF] David W Reduker | Ultrastructural changes in the oocyst wall during excystation of Cryptosporidium parvum (Apicomplexa; Eucoccidiorida)[END_REF]. The free form will enter the cell and be converted into a trophozoite [START_REF] Chen | Cryptosporidium parvum is cytopathic for cultured human biliary epithelia via an apoptotic mechanism[END_REF]. Subsequently, the trophozoite evolves into type I meront. Afterwards, certain type I meronts develop into type II meronts and release free forms.

Existing work -Up to now, only one research has been conducted based on the analysis of microscopic images of Cryptosporidium parvum. In ).

[106], artificial neural networks (ANNs) were employed to identify the Cryptosporidium parvum oocysts. A total of 525 images were cropped to 36 x 36 pixels, and the cropped images were divided into two categories:

Other parasite image analysis

oocyst and non-oocyst images. To convert the digital images into the format used by the neural network, the authors chose a relatively simple algorithm: a histogram measuring the pixel intensity of grayscale images. Afterwards, this network was optimised by applying different quantities of training images and hidden neurons. The results indicated that the artificial neural network obtained satisfactory performance in test images. In several tests, the correct recognition rates ranged from 81% to 97% for oocyst images and 78% to 82% for non-oocyst images.

Other parasite image analysis

In addition to the existing works on the Malaria and Cryptosporidiumparasite image analysis, we also found a number of relevant works related to other parasites [START_REF] Roder | Intestinal parasites classification using deep belief networks[END_REF][START_REF] Yoselyn | Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification[END_REF][START_REF] Osaku | Automated diagnosis of intestinal parasites: a new hybrid approach and its benefits[END_REF][START_REF] Zhang | Deep learning for imaging and detection of microorganisms[END_REF][START_REF] Górriz | Leishmaniasis parasite segmentation and classification using deep learning[END_REF]. Three studies regarding the classification of intestinal parasite images used different methods [START_REF] Roder | Intestinal parasites classification using deep belief networks[END_REF][START_REF] Yoselyn | Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification[END_REF][START_REF] Osaku | Automated diagnosis of intestinal parasites: a new hybrid approach and its benefits[END_REF]. Roder et al. [START_REF] Roder | Intestinal parasites classification using deep belief networks[END_REF] were the first to experiment with Deep Boltzmann Machine and Deep Belief Network for parasite classification, and the robustness of the model was confirmed by experiments using 3 different datasets including fecal impurity surrounds. Machaca et al. [START_REF] Yoselyn | Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification[END_REF] presented Chapter 2. State of the art of parasite image analysis a method for generating synthetic images of parasites using Deep Convolutional Generative Adversarial Network (DCGAN), followed by experiments using the pre-trained deep learning models Resnet34, Resnet50, VGG16, Densenet121, and Inceptionv3. The experimental results demonstrated the accuracy improvement with the use of synthetic datasets. While a hybrid method was proposed by Osaku et al. [START_REF] Osaku | Automated diagnosis of intestinal parasites: a new hybrid approach and its benefits[END_REF], which combines the views from two decision systems (support vector machines and deep neural networks VGG16) with complementary properties so as to enhance the overall efficiency.

In addition, in a study on microorganisms [START_REF] Zhang | Deep learning for imaging and detection of microorganisms[END_REF], some novel deep learning methods for parasite microscopic image recognition integrated the geometric morphology of parasites into deep learning framework to reduce the dependence on labeled data [START_REF] Li | Transfer learning for toxoplasma gondii recognition[END_REF][START_REF] Li | Parasitologist-level classification of apicomplexan parasites and host cell with deep cycle transfer learning (DCTL)[END_REF][START_REF] Jiang | Geometry-aware cell detection with deep learning[END_REF]. For example in [START_REF] Li | Parasitologist-level classification of apicomplexan parasites and host cell with deep cycle transfer learning (DCTL)[END_REF], microscopic parasites (Toxoplasma, Plasmodium, Babesia) were linked to their morphologically similar macroscopic objects (banana, ring, and pear) by deep cycle transfer learning (illustrated in Fig 2 .6). The results showed the high accuracy of the proposed method, with an average accuracy of more than 95% for all parasite types.

What's more, Górriz et al. [START_REF] Górriz | Leishmaniasis parasite segmentation and classification using deep learning[END_REF] trained a U-Net model that can successfully segment leishmania parasites (displayed in Fig 2 .7) and classify them into 3 classes: promastigotes, amastigotes, and adhered parasites. The authors thought that the improvement could be applied by using larger databases, keeping the balance of the classes, and providing a precise annotation.

Conclusion

Among the state of the art of parasite analysis from microscopic images presented in this chapter, it is worth mentioning that the Malaria studies take up the major part. Although the performances of the existing systems are promising, comparisons among different systems are quite challenging. The generality of the systems is also difficult to guarantee since most of the datasets used for assessing performance are not publicly available. FIGURE 2.6: Microscopic parasites (Toxoplasma, Plasmodium, Babesia) were linked to their morphologically similar macroscopic objects (banana, ring, and pear) (extracted from [START_REF] Li | Parasitologist-level classification of apicomplexan parasites and host cell with deep cycle transfer learning (DCTL)[END_REF]).

While for Cryptosporidium, there has been few vision-based works for the analysis of this parasite. Additionally, we have also presented the life cycle of Cryptosporidium and the evolution of each life stage in the asexual phase.

FIGURE 2.7: Annotation of leishmaniasis parasite

(extracted from [START_REF] Górriz | Leishmaniasis parasite segmentation and classification using deep learning[END_REF]).

Chapter 3

Generalized deep learning for

Plasmodium parasite image analysis

Introduction

Malaria is one of the epidemics that possess the potential to threaten human life [START_REF] Alan F Cowman | Malaria: biology and disease[END_REF][START_REF] Wang | Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015[END_REF]. In fact, during the year of 2018 there were an estimate of 228 million cases of Malaria, resulting in over 400,000 fatalities around the world. Of this group, children younger than 5 years of age represent to be the most fragile, with about one death under 2 minutes, constituting over 65% for all deaths due to Malaria [START_REF]World Malaria Day 2020 : Malaria -Status Update on Children[END_REF][START_REF]World malaria report[END_REF]. Malaria is caused by Plasmodium parasite and is transmitted through the bite of infected female Anopheles mosquitoes [START_REF] Tuteja | Malaria-an overview[END_REF]. Malaria is widespread in tropical and subtropical areas, particularly in Asia, Sub-Saharan Africa and Latin America [START_REF] Caraballo | Emergency department management of mosquito-borne illness: malaria, dengue, and West Nile virus[END_REF]. Globally, nearly 85% of Malaria deaths occurred in Africa and India during 2018, with 24% in Nigeria [START_REF]World malaria report[END_REF]. In WHO's Southeast Asia and Africa region, the number of mortality has declined from approximately 530,000 (2010) to 380,000 (2018). Nevertheless, since 2016, the decrease in mortality rate has decelerated [START_REF]World malaria report[END_REF].

There are a couple of ways for diagnosing Malaria [START_REF] Mbanefo | Evaluation of malaria diagnostic methods as a key for successful control and elimination programs[END_REF], of which microscopic examination of thick or thin blood smears is the gold standard [START_REF] Mbanefo | Evaluation of malaria diagnostic methods as a key for successful control and elimination programs[END_REF][START_REF] Makhija | The utility of serial blood film testing for the diagnosis of malaria[END_REF]. To be more specific, blood samples are colored according to the Chapter 3. Generalized deep learning for Plasmodium parasite image analysis

Giemsa method [START_REF] Piaton | Technical recommendations and best practice guidelines for May-Grünwald-Giemsa staining: literature review and insights from the quality assurance[END_REF] and then a well-trained expert analyzes them with a microscope to detect parasites in the blood cells as well as to determine their species. In fact, human Malaria infections are due to Plasmodium parasites consisting of no less than six species, namely P. falciparum, P. malariae, P. ovale (two variants), P. vivax and P. knowlesi [START_REF] Singh | Human infections and detection of Plasmodium knowlesi[END_REF][START_REF] Lalremruata | Species and genotype diversity of Plasmodium in malaria patients from Gabon analysed by next generation sequencing[END_REF]. These species may contribute to the death of patients through infection with red blood cells [START_REF] Andrew | Malaria elimination: time to target all species[END_REF]. Therefore, regardless of the species of Malaria parasite, an early diagnosis is necessary in order to i) enable the patient to receive timely treatment [START_REF] Mehta | Detection Of Malaria Parasite Using Deep Learning[END_REF], ii) limit the transmission of the infection via mosquitoes within the community [START_REF] Chen | A study of image segmentation algorithms combined with different image preprocessing methods for thyroid ultrasound images[END_REF]. However, specialist microscopic examination is still extremely time-consuming and challenging since parasites are typically tiny and potentially able to be mistaken with non-parasitic objects [START_REF] Tangpukdee | Malaria diagnosis: a brief review[END_REF][START_REF] Mehanian | Computer-automated malaria diagnosis and quantitation using convolutional neural networks[END_REF][START_REF] Hathiwala | LED fluorescence microscopy: Novel method for malaria diagnosis compared with routine methods[END_REF]. Furthermore, the examination is suggested to be completed by two specialists [START_REF] Bailey | Guideline: the laboratory diagnosis of malaria[END_REF] to achieve a more solid diagnostic result. There is indeed a significant risk to the patient that inaccurate diagnosis may lead to inappropriate treatment [START_REF] Dn Durrhelm | Diagnostic disagreement-the lessons learnt from malaria diagnosis in Mpumalanga[END_REF].

The World Health Organization (WHO) has encouraged research on appropriate approaches for a fast and economical diagnosis to treat Malaria [START_REF]Global technical strategy for malaria 2016-2030[END_REF]. Hence, there is a need to develop an automated Malaria diagnostic system that is not only reliable, but can be faster and more economical, especially in resource-limited regions [START_REF] Yang | Deep learning for smartphone-based malaria parasite detection in thick blood smears[END_REF].

Artificial intelligence (AI) currently provides the possibility to achieve this goal; whereas in scientific community the employment of deep learning techniques is now the obvious alternative for medical image analysis [START_REF] Litjens | A survey on deep learning in medical image analysis[END_REF]. All these techniques offer strong aids for specialists to execute highlevel medical tasks, for instance, diabetic retinopathy detection [START_REF] Varun Gulshan | Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs[END_REF] or skin cancer classification [START_REF] Esteva | Dermatologist-level classification of skin cancer with deep neural networks[END_REF]. In this study, we propose a framework for diagnosing Malaria infection in humans using microscopic images of thin blood smears. To be specific, the framework aims at segmenting the Plasmodium parasites and distinguishing the species into four major classes: P. Falciparum, P. Ovale, P. Malaria and P. Vivax.

The main contributions of our study are as follows:

Exploited CNN-based architectures

21

-A straightforward segmentation and classification method which relies on the parasite itself. Noteworthy is the fact that our method is complements to those proposed methods [START_REF] Maity | Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet[END_REF][START_REF] Delgado-Ortet | A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection[END_REF][START_REF] Molina | Sequential classification system for recognition of malaria infection using peripheral blood cell images[END_REF]. Although these methods are designed to quantify the amount of infected RBCs, our method enables to outline the shape of the parasite within these cells, thereby providing an accurate analytical tool.

-Our framework demonstrates the efficiency of generalization to interclass data on 2 Malaria datasets [START_REF] Loddo | MP-IDB: the malaria parasite image database for image processing and analysis[END_REF][START_REF] Syed Saiden | Malaria-Detection[END_REF]. These datasets consist of images that have extremely different features owing to variations in data collection conditions (e.g., microscope characteristics, illumination conditions, and magnification).

-

Exploited CNN-based architectures

In this section, we describe in detail the related architectures for image segmentation as well as image classification that have been employed in our work.

Object segmentation -Convolutional neural networks (CNN) can achieve image classification, but how to identify objects in specific parts of images through deep learning has always been a problem. Until 2015, Long et al. [START_REF] Long | Fully convolutional networks for semantic segmentation[END_REF] proposed the Fully Convolutional Networks, leading the field of image semantic segmentation. FCNs can classify images at the pixel level, Chapter 3. Generalized deep learning for Plasmodium parasite image analysis thereby solving the problem of semantic segmentation. They have received increasing attention and have been employed successfully with good results in biomedical images, for example, liver and lesion segmentation in CT [START_REF] Ferdinand | Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields[END_REF] and cardiac segmentation in MRI [START_REF] Vu | A fully convolutional neural network for cardiac segmentation in short-axis MRI[END_REF]. Unlike convolutional neural networks, there is no fully connected layer in FCNs. FCNs adopt deconvolution layers to upsample the feature map of the last convolutional layer in order to the same size as the input image. In this way, a prediction is generated for each pixel, while retaining the spatial information in the original input image. Finally, the pixel-wise classification is implemented based on the feature map obtained after the upsampling operation. A) illustrates the general network of a FCN architecture. The network consists of the following basic layers, including convolution (conv), pooling (pool), activation and deconvolution (deConv) [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF]. Furthermore, the authors proposed two other FCN architectures that vary in upsampling scale: FCN-16s (Row B) and FCN-8s (Row C). They fuse the pooling information from different layers and can provide better semantic segmentation results than that of the original. The network architectures of FCN-16s and FCN-8s are also shown in (Row B is FCN-16s and Row C is FCN-8s). For instance, in FCN-8s, the coarse output of the FCN model is initially upsampled by a factor of 4, while the pool4 image is upsampled by a factor of 2. Subsequently, these upsampled images are fused with the pool3 layer image, which is finally upsampled by a factor of 8 to yield the predicted image of the same size as the input.

After series of successful applications of FCNs in semantic segmentation, numerous new algorithms based on FCNs techniques have been proposed. They are extensively investigated in the fields of image segmentation, classification, etc [START_REF] Wang | Visual tracking with fully convolutional networks[END_REF][START_REF] Maggiori | Fully convolutional neural networks for remote sensing image classification[END_REF]. As a variant of FCNs, U-Net architecture [START_REF] Ronneberger | U-net: Convolutional networks for biomedical image segmentation[END_REF] has been widely used in the field of cellular segmentation and biomedical image processing [START_REF] Schmidt | Cell detection with star-convex polygons[END_REF][START_REF] Stringer | Cellpose: a generalist algorithm for cellular segmentation[END_REF].

The architecture U-Net is shown in Fig 3 .2. It includes repeatedly applying two 3x3 convolution operations, ReLU activations, and a downsampling operation through 2x2 max-pooling with stride 2. Between some FIGURE 3.1: Fully convolutional neural networks (FCNs) [START_REF] Long | Fully convolutional networks for semantic segmentation[END_REF].

convolutional layers, dropout is adopted. The deconvolution process comprises an upsampling of the feature maps, subsequent a 2x2 convolution, concatenation of the corresponding feature maps from the previous convolutional layers, and two 3x3 convolutions with ReLU activations. In contrast to the original U-Net, a 3x3 convolution (including two filters) layer is applied before the output. The last layer is employed to map feature vectors to the required number of classes to generate the result of pixel-wise segmentation.

Object classification -In this section, I will compare several architectures that are frequently used in recent years. AlexNet

AlexNet [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] is the first large-scale convolutional neural network architecture performing well on ImageNet [START_REF] Deng | Imagenet: A large-scale hierarchical image database[END_REF] classification. AlexNet participated in the competition and outperformed previous non-deep learning based models with remarkable margins.

AlexNet was trained on ImageNet and the input size was 227 x 227 x 3. The AlexNet architecture consists of convolutional layers, pooling layers and fully connected layers. As is displayed in Fig 3 .3), there are five convolutional layers, starting with a convolutional layer with a kernel In LSVRC-2014, VGG16Net [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] won the competition on ImageNet with 92.7% accuracy, reducing the error of the AlexNet by a factor of 2. There are two versions of VGGNet, VGG16 and VGG19. The VGG19 architecture (also known as VGGNet-19) is very similar to the VGG16, but has more convolutional layers in it (see in Fig 3.4). Inception

Inception v3 [START_REF] Szegedy | Rethinking the inception architecture for computer vision[END_REF] is a combination of many ideas developed by several researchers for many years and has been proven to yield an accuracy of over 78.1% on the imageNet.

Inception consists of symmetric and asymmetric blocks, including convolutional layers, max pooling, average pooling, dropout and fully connected layers. Its main idea is to merge convolutional kernels in parallel, as shown in

Methodology

Our Malaria diagnostic system consists of a number of steps, as shown in Fig 3 .8. To be specific, taking as input an image stack of the patient sample obtained by microscope, the linear interpolation-based downscaling operation (Step 1) is applied to these images in order to achieve 224 by 224. In fact, through the analysis of the Malaria public datasets, we observed that the captured images have a high resolution (ranging from 640 x 480 to 2592 x 1944), which is unsuitable for direct processing with deep architectures. In order to achieve this goal, we have adopted a small square resolution, which has been widely used in the field of deep learning. Based on this, the experts can take advantage of two possibilities: (1) the classification by the analysis to distinguish (Step 5.1 -5.3), (2) to supply indicators, like the number of parasites detected and fine delineation of parasite shape (Step 5 ').

For the prediction of parasite species, the parasite with the largest area is chose out of each image and extracted through a square window of 224 x 224 pixels centered on the parasite before cropping. Then, we classify the parasite crops into 4 classes: P. Falciparum, P. Malariae, P. Ovale, and P. Vivax. Finally, a major voting method is used to determine the infected parasite species based on the previous classification results. If there is no parasite found in the segmentation step, then the classication step will output no Malaria infection.

It should be noted that the selection of 224 x 224 for image crops is appropriate in this situation, as it has been found that in the majority of cases this resolution is enough to include a single parasite (99% of the parasite in all data sets is less than 224 × 224). We also downscale the larger parasite crops to 224 x 244 (their width or height is no more than 350 pixels). In addition, we consider only the largest parasite in each image, so as to improve the probability of detection of a true positive.

Parasite segmentation

In order to extract potential parasites from the input image, we used a pixel based method to segment them. Specifically, a label is allocated to every pixel in an input image, i.e., a foreground and a background. The foreground category includes the pixels of the subject of interest, which are parasites, whereas the background class includes other pixels in the picture, such as uninfected cells, extracellular fluid. Therefore, the classification of the individual pixels results in the creation of a binary mask that allows to delimit the the contour of the parasite from the image. We use the U-Net architecture as the kernel of our image segmentation algorithm. This architecture consists of an encoder and a symmetric decoder. The encoder allows the input image to be scaled down by passing it into a series of convolution-layer blocks [START_REF] Maggiori | Fully convolutional neural networks for remote sensing image classification[END_REF]128,256,512), with an intermediate maxpooling operation followed by a stack of 1024 small contextual feature maps as output. The symmetric decoder allows to upgrade the generated feature maps from the encoder by passing them into an inverted sequence of convolution layers (512,256,128,[START_REF] Maggiori | Fully convolutional neural networks for remote sensing image classification[END_REF]. Each of these blocks is preceded by an intermediate up-convolution operation and produces a stack of feature maps with size 64 as output. In order to improve the positioning accuracy of the target, the authors proposed that the convolution layer block of the encoder be connected to it, which is the equivalent (in terms of the number of layers). Then there is a second set of 64 convolution layers, which is followed by the binary mask.

In order to improve the performance of the U-Net, we customized a variant by integrating a deep architecture VGG19 [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] that is tailor-made for image classification. In particular, we used VGG19 instead of the original U-Net encoder. By this way, we followed the same principles as U-net to construct the symmetric decoder.

Based on the Jaccard Index of Eelbode et al. [START_REF] Eelbode | Optimization for medical image segmentation: theory and practice when evaluating with Dice score or Jaccard index[END_REF] we kept this measure and modified it to solve the problem of segmentation. In particular, the variation has been trained to minimize the Jaccard loss (Jl) between an image's predicted binary mask and its associated ground truth mask. To define this loss, we first formulate our parasite segmentation problem as follow:

Let bm a be the binary mask obtained by our approach for an image Img(w, h), where (w, h) represent the image's width and height. We denote the label of a pixel p i, j positioned at indexes (i, j) of Img by l i, j bm a in the binary mask bm a , where:

l i, j bm a = 1 i f p i, j ∈ parasite 0 else (3.1)
Similarly we define bm gt as the ground-truth binary mask that corresponds to a manual segmentation of Img performed by a specialist.

The Jl between two masks is defined as:

Jl(bm a , bm gt ) = 1 -Ji(
       (bm a ∩ bm gt ) = ∑ h,w i, j I(l i, j bm a = l i, j bm gt = 1), I : binary indicator (bm a ∪ bm gt ) = ∑ x=a,gt ∑ h,w i, j I(l i, j bm x = 1) -(bm a ∩ bm gt )
The Jl permits the measurement of the difference between the two segmentations and outputs in the range [0, 1] where 0 indicates that the two segmentations are the same.

To minimize Jl, the architecture's weights are optimized according to the adaptive moment optimization technique (Adam) [START_REF] Choi | On empirical comparisons of optimizers for deep learning[END_REF], which is a stochastic gradient descent technique based on an adaptive estimation of the first and second order moments. We denote w i j t a weight to update between two neurons x i and x j of our architecture for step t + 1 of the training:

w i j t+1 = w i j t - η • m t+1 v t+1 + ε with (3.4)                      m t+1 = m t+1 /(1 -β t+1 1 ) v t+1 = v t+1 /(1 -β t+1 2 ) m t+1 = β 1 m t + (1 -β 1 ) • δ j t , β 1 = 0.9, m 0 = 0 v t+1 = β 2 v t + (1 -β 2 ) • δ 2 j t , β 2 = 0.999, v 0 = 0 η = 10 -3 , learning rate ε = 10 -8 , calculation stability
Here the (β 1 , β 2 ) are the exponential decay rates for the first and second moment respectively and δ j t is the gradient error back-propagated from the loss Jl over the neuron x j at step t of the backward pass.

Parasite classification

To implement our classification module, we developed our own deep architecture called Light-Net, that is based on a standard CNN. As shown in Figure 3.9, the architecture consists of 3 blocks of convolutional layers, each followed by a Relu : max(x, 0) activation function and a max pooling operation. The convolutional sequence is followed by a global average pooling, then by a dense layer of 64 units, with a Relu activation and an output dense layer of 4 units that corresponds to the four species to be predicted. For this purpose, the output layer is related to a so f tmax function to calculate the species' probability. The architecture has been trained to minimize the categorical cross entropy loss L for the crop classification defined below:

for the crop classification of an image defined as follows. where y c is a binary indicator whether the class c is the correct one for the given crop and p c is the predicted probability by the model on the same crop for being of class c. The weights have been updated based on a Root Mean Square propagation technique (RMSprop) [START_REF] Choi | On empirical comparisons of optimizers for deep learning[END_REF]. We denote w i j t a weight to update between two neurons x i and x j of our architecture Chapter 3. Generalized deep learning for Plasmodium parasite image analysis for step t + 1 of the training:

           w i j t+1 = w i j t -( η √ v t +ε * δ j t ) v t = β v t-1 + (1 -β )δ 2 j t , β = 0.9, v 0 = 0 η = 10 -3 , learning rate ε = 10 -8 , calculation stability (3.6)
where δ j t is the gradient error of the neuron x j back-propagated from the loss L on the predicted class for the input image at step t of the backward pass. 3.4 Experimental study

Datasets analysis

To explore the possibility of generalization, we have explored 6 public Malaria datasets. It should be noted that, the Malaria diagnosis in all samples was performed according to the May-Grünwald-Giemsa (MGG) method [START_REF] Piaton | Technical recommendations and best practice guidelines for May-Grünwald-Giemsa staining: literature review and insights from the quality assurance[END_REF]. However, the images were obtained with a variety of microscopes and different parameters, in particular with respect to magnification and illumination. Even within the dataset, these parameters may differ. As a result, the images display large differences in resolution, colour, contrast, brightness and texture (shown in Figure 3.11). We also created a global training set (hybrid set) by combining the two datasets named Dataset 12 , including 1559 images. We artificially increased the training set of Dataset 1 from 154 to 924 images so as to relatively balance the two datasets (924 and 635 respectively) in a relative manner. For this purpose, we applied five transformations on Dataset 1 , as follows: (1) two spatial transformations, involving horizontal and vertical flips;

(2) three pixel-based transformations, covering integrated contrast and brightness, CLAHE and Gaussian noise.

Segmentation performance analysis

We assessed the quality of our segmentation module using the Average Precision metric (AP), which is often employed for the assessment of biomedical image segmentation (e.g [START_REF] Schmidt | Cell detection with star-convex polygons[END_REF][START_REF] Stringer | Cellpose: a generalist algorithm for cellular segmentation[END_REF]). In our case, the metric allows to calculate the ratio AP = T P/(T P + FP + FN) between the number of correctly detected parasites True Positive (TP) and incorrectly detected parasites False Positive (FP) as well as missed ones False Negative (FN). For this purpose, TP, FP and FN are determined compared to the ground-truth of parasites based on a matching threshold which corresponds to the Jaccard index (see equation 3.3) in the range of [0.5, 1]. The value 0.5 is a common threshold for assessing segmentation quality. In our case, if the parasite detected has at least 50% of the area matches the area of the parasite in the ground truth, then it is detected as a TP.

Our segmentation architecture called U-Net_VGG19 has been trained and validated on:

• The training / validation sets of the two datasets separately which allow for the production of two specialized segmentation models U-Net_VGG19_Dataset 1 and U-Net_VGG19_Dataset 2

• The training / validation sets of the two datasets together (Dataset 12 ) which allow for the production of generalized segmentation model U-Net_VGG19_Dataset 12 .

We emphasize that among the five widely used optimizers (Adam, SGD, RMSprop, Adagrad, Adadelta), we observed that the Adam performed the best.

Chapter 3. Generalized deep learning for Plasmodium parasite image analysis

For ease of reading, we denote U-Net_VGG19_Dataset 1 , U-Net_VGG19_Dataset 2 and U-Net_VGG19_Dataset 12 parasite segmentation models as model 1, model 2 (specialized models) and generalized model respectively.

Figure 3.14 illustrates the AP curves of specialized and generalized models generated on the test sets of Dataset 1 on the left (179 parasites) and Dataset 2 on the right (485 parasites). The curves indicate that while the specialized models are incapable of detecting parasites on the external data (AP Ji@0.5 = 0.03 for model 1 on Dataset 2 and model 2 with an AP Ji@0.5 = 0.04 on Dataset 1 ), the generalized model successfully segmented parasites on both datasets with promising precision (AP Ji@0.5 = 0.73 for both datasets). Additionally, the curves reveal that the generalized model outperforms the specialized model 1 on the test set of Dataset 1 and performs similarly compared to the specialized model 2 on the test set of Dataset 2 (with a difference of about 0.01 at each step of the Ji threshold). It can be also observed that the precision of the models drop when the Ji threshold increases, suggesting that it is difficult to align the ground-truth parasite contours with the automatic ones, especially for Dataset 1 . Nevertheless, as demonstrated in Figure 3.15, the segmentation results obtained by the generalized model on both datasets (bottom rows of subfigures 3.15a and 3.15b) seem to be very close to the ground-truth in shape and number, regardless of the parasite species (top rows of subfigures 3.15a and 3.15b). The early drop in model precision on Dataset 1 might be partly explained by the small size of the parasites areas. Indeed, in contrast to Dataset 2 , most parasites in Dataset 1 are characterized by a small surface which may make it challenging to match the area between automatic and ground-truth parasites. Furthermore, due to the small area of the parasites, we believe that it is difficult to obtain a high level of area matching even between two human experts' segmentations. We can also observe on Figure 3.16 that the amount of false parasites (FP) is relatively low. After visually analyzing the results for both datasets, we noticed that FP parasites are not necessarily false. In fact, as indicated in Figure 3.17 for both datasets some parasites (delineated in orange) have been missed in the ground-truth. We note that these parasites are usually small and located at the boundaries of the images. Except for our proposed U-Net_VGG19 generalized model, we also trained two other state-of-the-art architectures, the original U-Net and the U-Net_ResNet34 following the same training / validation rules. We also compared the parasite segmentation results of our generalized model with the results of Abbas and Dijkstra's segmentation method [START_REF] Syed Saiden | Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers[END_REF] (owners of Dataset 2 [START_REF] Syed Saiden | Malaria-Detection[END_REF]). Since the authors trained their segmentation model on Dataset 2 using a cross-validation method it would be impossible to make a fair comparison on this dataset. For this reason, we compared the two segmentation models on a sample of 13 images from the CDC (Control Disease Center), and the authors have shared the associated automated segmentation results as figures in the supplementary material document (referenced in [START_REF] Syed Saiden | Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers[END_REF]). To be specific, we used the screenshots of these images to test our model and generate associated segmentation results. Figure 3.19 displays comparative examples of the respective results for the first three images shared in their document (more comparative results can be found in the supplementary file). In addition to the fact that we tested our model on screen-shot images, it is worth mentioning that we did not apply any cell diameter-based image rescaling or surface threshold-based object suppression, as was done in [START_REF] Syed Saiden | Detection and stage classification of Plasmodium falciparum from images of Giemsa stained thin blood films using random forest classifiers[END_REF]. One may note that our method has finer contour segmentation. However, some parasites are missed, probably partly due to the degraded quality of the screen-shot images. We mainly used Dataset 1 because it is the only dataset that contains 4 species. To be specific, we used the same training / validation / test sets as the segmentation part (see Dataset 1 in Figure 3.13). The first three columns of Table 3.2 show the data split details of the species. It can be observed that the size of the training set is too small to train our classification architecture. For this reason, we proposed a first step for parasite data augmentation using our LPTS (Local Parasite Texture Scanning) approach.

Local Parasite Texture Scanning (LPTS) -Figure 3.20 shows an example of the output when our method is applied to a parasite. To be more specific, the parasite is first bounded on the original image (red box in subfigure 3.20a) according to the associated segmentation in the binary mask (subfigure 3.20b). Then the bounding box is enlarged to a 224 x 224 square window in the center of the parasite and shifted in 8 directions (black square in subfigure 3.20a and 3.20b) in order to generate 9 binary masks crops (subfigure 3.20c) and associated original image crops (subfigure 3.20d) according to the method given in section 3.3.3. Though the method is based on image shifting, it allows to increase effectively the number of images and to diversify the configuration of parasites by locally analysis changing their position / shape / number. The last three columns of Table 3.2 indicate the amount of parasites crops per species obtained after applying our approach to the original data of Dataset 1 .

Classification performance analysis

To assess the performance of the parasite species classifier, we employed standard scoring metrics including accuracy, precision, recall and confusion matrix [START_REF] Ryan | ExSTraCS 2.0: description and evaluation of a scalable learning classifier system[END_REF]. Our classification architecture has been trained and validated on the "Crops LPTS + standard augmentation" training / validation sets (see last 2 columns of Table 3.2) and tested on the test set (see 6 th column of Table 3.2). We highlight that among the five commonly tested optimizers (Adam, RMSprop, SGD, Adadelta, Adagrad), the best performance was observed for RMSprop. To determine whether our LPTS technique helps to improve the performance of the classifier, we trained our architecture on a reduced training set, keeping only the window crops in the center of the parasite (see position 1 in subfigures 3.20c and 3.20d) and removing the crops in the remaining 8 positions. We also compared the performance of our classification architecture with the state-of-the-art by experimenting Xception [START_REF] Chollet | Xception: Deep learning with depthwise separable convolutions[END_REF], ResNet50 [START_REF] He | Deep residual learning for image recognition[END_REF] and VGG16/19 [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] architectures. For this purpose, we employed a transfer learning strategy to fine-tune the associated models pre-trained on the ImageNet dataset [START_REF] Deng | Imagenet: A large-scale hierarchical image database[END_REF].

The architectures have been tuned by keeping the default sequence of convolution layer blocks, adding a global average pooling and an output layer of four units (corresponding to the four classes of the parasite). The finetuning step has been performed during the training by freezing the Ima-geNet weights for the sequence of convolutional layer blocks and optimizing the weights of the newly added dense layer. These adapted versions have been trained, validated on the same "Crops LPTS + standard augmentation" training / validation sets as our architecture and tested on the same test set. Among the four tested architectures, only VGG16 achieves competitive performance compared to our architecture. Table 3.3 displays the performance obtained by our architecture on two training strategies (with and without LPTS crops) and VGG16 on the validation set. It can be noticed that our architecture achieved the best performance for the four species. Furthermore, the table shows that our LPTS technique enabled our architecture and VGG16 to outperform the degraded version (Light-Net*), improving considerably their discrimination ability Chapter 3. Generalized deep learning for Plasmodium parasite image analysis for different species, especially for Falciparum and Ovale species. Figure 3.21 displays the confusion matrices obtained on the test set by our architecture and the VGG16 [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] when trained on the "Crops LPTS + standard augmentation" training set. The matrices reveal that our architecture outperforms the VGG16 on three species (Falciparum, Ovale and Vivax), while the VGG16 is slightly better at identifying the Malariae species. The average accuracy on the four species is 88% for our architecture vs 78% for VGG16. A final test of the two architectures has been done on a test set from Dataset 2 consisting of 5889 Falciparum parasite crops. These crops have been generated from the validation / test sets used in the segmentation part (see Dataset 2 in Figure 3.13). Table 3.4 displays the classification results. We observe that the discrimination ability of our architecture is confirmed as it detects 99.5% of crops as Falciparum species and outperforms the VGG16. Additionally, the results show that our architecture has a high generalization potential on Dataset 1 and Dataset 2 .

3.4.4 Global system results and evaluation

Chapter 3. Generalized deep learning for Plasmodium parasite image analysis

We remind that we used the same test sets from Dataset 1 and Dataset 2 in the segmentation and classification modules, so we can use these test sets to evaluate the global system (combined segmentation and classification) at the patient level (see Figure 3.8). Table 3.5 displays examples of the results obtained by our global system on 10 patients from Dataset 1 and Dataset 2 . One can note that all the patients have been diagnosed with the correct species. In fact, this result is confirmed for all patients in both datasets. It can also be observed that the predicted number of parasites is close to the ground-truth. All these results clearly indicate that merging inter-class data represents a promising direction of investigation to develop powerful generalized systems based on deep learning. This section presents our global system results on the 4 test datasets (Dataset 3,4,5,6 ) in Table 3.1 by its segmentation and classification modules. Since these datasets do not provide a segmentation ground-truth of the parasite, we assessed our system by calculating the parasite detection rate. It corresponds to the ratio of the number of images in which the segmentation module detected at least one parasite (i.e. image detected as infected) divided by the total number of images (T P/(T P + FN)). We precise that all images of these 4 datasets are labeled as infected, which implies that all of them should contain at least one parasite. The results are summarized in the Table 3.6. Notably, the module is not suitable for segmenting Dataset 4 , as it contains individual cell images. The table demonstrates that 3.4. Experimental study 51 our segmentation module has successfully detected at least one parasite in each image, with high detection rates (over 99%) in Dataset 3 , Dataset 5 and an acceptable one (85%) in Dataset 6 . Figure 3.22 shows some qualitative results of the segmentation obtained by our module on the datasets. For the classification part, our system selected the largest parasite detected by the segmentation module from each image and classified its species, then following a major voting on the images of the same patient and the system output the final predicted species. The Major voting is performed only in Dataset 4 because the images are organized by patient, while in Dataset 5 and Dataset 6 , where no information is provided about the patient, the system outputs the predicted species of the largest parasite detected in each image. In Dataset 3 it is impossible to assess our classification module because no information on species is provided. The results are summarized in the Table 3.6. The table reveals that for Dataset 4 , the classification module has a reduced discrimination capacity for P. Falciparum. This is due to the particular form of images considered (individual cell images rather than LPTS crops). Nevertheless, the module succeeded in identifying the Falciparum species in Dataset 5 , reaching 84%. We believe in this data set, the accuracy of our system for species identification at the patient level may be higher, as classification decisions will be based on a major voting. Finally, we note that there is a decrease of performance in Dataset 6 which involves Vivax species. This result was expected because our generalized classification module has been trained on mixed data for Falciparum species (data from Dataset 1 and Dataset 2 ) but not for the other Chapter 3. Generalized deep learning for Plasmodium parasite image analysis species due to lack of data. We believe that mixing data for Malariae, Ovale and Vivax species may further improve the generalization potential of our classification module.

Performance analysis on other datasets with partial groundtruth

Conclusion

We have proposed a framework for diagnosing Malaria infection in humans using microscopic images of thin blood smears. To be specific, this framework aims to segment the Plasmodium parasite and to differentiate between its species in four dominant classes: P. Falciparum, P. Ovale, P. Malaria and P. Vivax. For this purpose, two deep learning architectures based on CNN, called U-Net_VGG19 and Light-Net, have been performed for the segmentation and classification steps of parasites, respectively. Both architectures have been trained with the generalization of the resulting models in mind, especially on inter-class data. By combining two public datasets [START_REF] Loddo | MP-IDB: the malaria parasite image database for image processing and analysis[END_REF][START_REF] Syed Saiden | Malaria-Detection[END_REF] and a parasite data augmentation technique called LPTS, our training strategy demonstrated its efficiency in improving the performance of the global system. Indeed, the resulting generalized segmentation model performed well for detecting parasites (accuracy between 85% and 99%) in a couple of public datasets [START_REF] Delgado | Dataset B: 331 digital images of MGG-stained blood smears from five malaria-infected patients[END_REF][START_REF] Boray Tek | Parasite detection and identification for automated thin blood film malaria diagnosis[END_REF][START_REF] Ljosa | Annotated high-throughput microscopy image sets for validation[END_REF]. However, more interclass data for parasite species could further optimize the classification module of the global system.

Chapter 4

Granularity guided deep learning for precise Cryptosporidium parasite image analysis

Introduction

Cryptosporidiosis is a diarrheal disease caused by the Cryptosporidium parasite, which may live in the intestine of several mammals. Currently 40 species were discribed in this gender. Among these last, C. parvum has been a primary issue of concern because it appears in the majority of human and livestock infections. Moreover, this species presents zoonotic properties and can therefore be transmitted from humans to animals and vice versa [START_REF] Jennifer | Cryptosporidium spp. diagnosis and research in the 21st century[END_REF][START_REF] Feng | Genetic diversity and population structure of Cryptosporidium[END_REF]. In humans, infected people might present with some symptoms, particularly diarrhea, abdominal pain and acute gastroenteritis [START_REF] Hatam-Nahavandi | Cryptosporidium infections in terrestrial ungulates with focus on livestock: a systematic review and meta-analysis[END_REF][START_REF] Gerace | Cryptosporidium infection: epidemiology, pathogenesis, and differential diagnosis[END_REF]. It is important to note that Cryptosporidium is the 5th causative agent of diarrhoeal disease in young children under 5 years of age. Furthermore, its presence has been implicated in increased mortality among young children [START_REF] Kotloff | Burden and aetiology of diarrhoeal disease in infants and young children in developing countries (the Global Enteric Multicenter Study, GEMS): a prospective, casecontrol study[END_REF]. Within livestock, cattle (especially calves) are considered to be the main reservoir of this parasite. In fact, over one million oocysts per gram of faeces can be excreted by an infected animal [START_REF] Blackburn | Cryptosporidiosis associated with ozonated apple cider[END_REF]. The VIDA (Veterinary Investigation Diagnosis Analysis) 2014 report indicated that Cryptosporidium was one of the four main causes of diarrhoea in calves during the period 2007 to 2011. This parasite also contributes to the death Chapter 4. Granularity guided deep learning for precise Cryptosporidium parasite image analysis of small animals and it has a significant economic impact by reducing the rentability of farms [START_REF] Apha | Veterinary Investigation Diagnosis Analysis (VIDA) report[END_REF]. Apart from labour expenses, the treatment of a sick calf costs a minimum of £34 [START_REF] Thomson | Bovine cryptosporidiosis: impact, host-parasite interaction and control strategies[END_REF].

It is therefore clear that controlling parasite colonisation would offer genuine benefits to the economy and would contribute to limiting the risk to health of humans. Nevertheless, achieving this goal is a challenge due to: i) the high level of resistance of the parasite to environmental condition, ii) the lack of effective medicines for infected animals as well as humans [START_REF] Chellan | Recent developments in drug discovery against the protozoal parasites Cryptosporidium and Toxoplasma[END_REF]. In such cases, developing a completely effective drug that targets this parasite would allow to prevent its reproduction on farms in the presence of infection. This lack of drugs is mainly attributed to the lack of automated and easily accessible tools to screen drug libraries. There are currently no highthroutput system to simplify the process of characterising new antiparasitic compounds.

The researchers initiate their experiments in vitro through the employment of cell layer infections when they wish to evaluate the efficacy of drugs against pathogens. After a period of incubation (varying from 24 hours to 72 hours within case of Cryptosporidium studies) the infected cells are fixed. The parasites are then specifically identified on the microscope slide using fluorescent-labelled antibodies [START_REF] Lichtman | Fluorescence microscopy[END_REF] (as shown in Fig 4 .1). Microscopic observation will result in an assessment of the reduction of parasite infections and differentiate their life stages (from asexual to sexual life stages). Nevertheless, it is a error-prone task for biologists to count the amount of parasites. For example, in a microscopic image captured with a 63 magnification (an image representing a view of relatively a small region of the infected sample), the number of parasites can reach on average a hundred after 24 hours of infection. Consequently, such manual method of analysis does not allow for a transition into large-scale trials in which thousands of molecules are used to accelerate drug discovery.

Additionally, direct exploitation of the existing methods is not sufficient to develop a reliable tool for the analysis of Cryptosporidium images due to the particular visual aspect of this parasite. Indeed, as illustrated To this end, we propose a coarse-to-fine segmentation approach to precisely segment a population of Cryptosporidium parvum parasites from a microscopic image by the first step. Then, a subsequent classifier with high discriminatory power is used to distinguish the life stages of the parasites among 4 asexual stages: oocyst, trophozoite, meront, and free form. To the best of our knowledge, this work is the first to address Cryptosporidium parvum analysis from microscopic images and offers a precision tool for assisting the biologists.

The main contributions of our work are summarized as follows:

-An original coarse-to-fine segmentation approach that permits to delineate precisely the contours of parasites whatever their shapes, their sizes and their situations in the image (grouped or isolated). To this end, the approach goes through three successive processing steps namely 1) a coarse segmentation step that outputs either individual parasites or grouped ones (i.e. they share common contours), 2) shape classification that permits to identify grouped parasites 3) contour refinement that permits to isolate each parasite from the -A classifier with high discriminative power for distinguishing the life stages of the parasites based only on the analysis of their visual appearance (reflected in the green channel of the microscopic image) and without the need of information related to the size and the number of nuclei which are required by the biologist. Indeed, the high performance obtained by the classifier on the experimented dataset shows that our deep learning architecture succeeded to characterize the parasite life stage from its visual appearance.

-A dataset of Cryptosporidium parvum parasite publicly available. The dataset is composed of 58 microscopic images acquired in our laboratory from infected HCT-8 cell lines (Declinai HCT). The images contain over 3,000 parasites which have been segmented manually and classified among four life stages by a biologist expert.

Exploited Transformer-based architectures

In this section, we mainly present the concept of Vision Transformer (ViT) and the related architectures applied to our research in this chapter. For other architectures of image classification and segmentation, we have already presented in the previous chapter. Chen et al. [START_REF] Chen | Transunet: Transformers make strong encoders for medical image segmentation[END_REF] proposed the first medical image segmentation architecture named TransUNet. Unlike [START_REF] Dosovitskiy | An image is worth 16x16 words: Transformers for image recognition at scale[END_REF], the authors did not split the image into patches, but used a series of convolutional operations for feature extraction. As displayed in parasites. 2) a shape classification model which allows to input cropped images of parasites that have been detected by previous step and outputs their classes (grouped or individual). 3) a fine segmentation model which inputs only the parasite crop identified as grouped in previous step and isolates them by delimiting each of their contours. Subsequently, a loop control module helps to detect outlier parasites. In addition, a classifier with high discriminatory power is used to distinguish the life stages of the parasites.

Coarse-to-fine segmentation module

Coarse-to-fine segmentation model includes the following steps: coarse segmentation and parasite cropping, parasite shape classification after cropping and fine segmentation.

Coarse segmentation

The segmentation architecture is designed to be identical for both coarse and fine segmentation step. The major difference lies in the image training set employed by the different models. To be more specific, the coarse segmentation model learns from a collection of images comprising a group of segmented parasites (the whole microscopic image), while the fine segmentation model learns from a collection of image crops consisting of a small group of segmented parasites with common outlines. Fig 4 .7 represents our implemented architecture TransUNet [START_REF] Chen | Transunet: Transformers make strong encoders for medical image segmentation[END_REF]. It is made up of two different processing modules, namely the encoder and the decoder. The encoder conducts feature extraction through a series of convolutional layers [START_REF] Chollet | Xception: Deep learning with depthwise separable convolutions[END_REF][START_REF] Esteva | Dermatologist-level classification of skin cancer with deep neural networks[END_REF][START_REF] Maggiori | Fully convolutional neural networks for remote sensing image classification[END_REF]128,256,512), each of which is followed by a max pooling operation. Afterwards, the linear projection operation converts the feature map from the convolution operation into matrices for passage over a series of transformer blocks. After rescaling the output of the transformer block, the hidden features are decoded by cascading several upsampling blocks to come up with the final segmentation mask, each containing a 2× upsampling operator, a 3×3 convolutional layer, as well as a ReLU layer.

We propose to employ a hybrid loss function to train the network, consisting of a Jaccard loss and a Cross-entropy loss, so that both local and global features are optimized:

Loss Jac = 1 - ∑ y i j t i j ∑ y i j + ∑ti j -∑ y i j t i j (4.1)
where y i j ∈ [0, 1] and t i j ∈ [0, 1] denote the output and target of each pixel at position (i, j). We adopt the Cross-entropy loss as an auxiliary loss in order to allow optimizing the local region of the image. It is formulated as:

Loss CE = - C ∑ i t i log(s i ) (4.2)
where t i and s i are the ground truth and the CNN score for each class i in C. Based on the two above losses, our loss function is as follows:

Loss our = αLoss Jac + (1 -α)Loss CE (4.3)
where α ∈ [0, 1]. In order to minimize the loss function, the Adam [START_REF] Diederik | Adam: A method for stochastic optimization[END_REF] optimizer is employed.

Parasite shape classifier after cropping

After coarse segmentation, we crop the detected parasites and regenerate them as small images with a resolution of 224 x 224. By analyzing the cropped parasite images, we find that most of the parasites exist in isolation, however there are some parasites in the form of parasite groups, as some parasites are interconnected. Even certain parasite groups contain parasites of different life stages (as displayed in Fig 4 .2(a)). Therefore, it is necessary to separate each parasite in the parasite group by a more refined segmentation before classifying the parasite life stages.

Compared to isolated parasites, we discover that parasite groups are usually more irregular in shape rather than close to circular. Therefore, we designed a custom architecture that incorporates CNN and transformer blocks for the construction of a parasite shape classifier. The inspiration for the architecture comes from the encoder of the segmentation architecture introduced in previous section.

As illustrated in Fig 4 .8, the architecture first consists of 10 convolutional layer blocks (5 successive double blocks), each of which is followed by a ReLU as activation function and a max pooling operation. After transforming the feature maps into matrices, the network is then connected to the six ViT (Vision Transformer) blocks. A flatten operation follows ViT blocks, which is followed by a dense layer of 256 units with a ReLU activation function. Eventually, an output dense layer of 1 unit is applied to gain the prediction probability for the shape of the parasite using a sigmoid activation function.

Fine segmentation

Considering the relatively regular shape of the parasites, we consider contour refinement of the parasite groups so as to isolate each parasite from the grouped parasites. Given that the shape of the parasites is known to be approximately circular, we select grouped parasite masks and perform parasite segmentation by manually filling black pixels for the contourconnected parasites (as displayed in Fig 4 .9). Afterwards, we apply the architecture as in coarse segmentation to train a deep learning network for fine segmentation using the parasite masks. The only difference is that the input size of the network is changed to 224 x 224. Subsequently, according to the fine segmentation of the mask, we obtain the result of the fine segmentation on the corresponding pixels of the original image.

Loop control module

After fine segmentation, we add a loop control module for detecting the number of parasite contours in each image. In the normal case, the connected parasites in the parasite group are separated and the module should detect more than 1 parasite at this point. However, if only 1 parasite contour is detected, it means that the previous coarse-to-fine segmentation module cannot handle the current parasite image. In this circumstance, we consider the parasite image as an outlier, and all subsequent outliers will be detected by the expert. Chapter 4. Granularity guided deep learning for precise Cryptosporidium parasite image analysis

Parasite life stage classification module

To classify all detected individual parasites in the framework, we train the deep learning network using approximately the same architecture as the parasite shape classifier. By comparison, the classification of parasite life stages requires the output to be changed to 4 instead of 1, and the corresponding activation function to be changed to softmax. In addition, the loss function is changed to categorical cross entropy instead of binary cross entropy.

Experimental study 4.4.1 Data preparation

Our dataset for the analysis Cryptosporidium parvum comprises 58 microscopic images. The images have been obtained by infection of HCT-8 cell lines with Cryptosporidium parvum in the laboratory (24 hours of infection). During the infection, the cells were fixed with methanol. Then the parasites are killed and stained with specific fluorescent markers. The parasite was detected with FITC-labeled Sporoglow antibodies marketed by Waterborne Inc. The stained parasites can be visible in the green channel of microscope. In the life cycle of Cryptosporidium parvum, we studied the asexual stage, containing four species: oocyst, trophozoite, meront, and free form [START_REF] Funkhouser-Jones | Defining stage-specific activity of potent new inhibitors of Cryptosporidium parvum growth in vitro[END_REF][START_REF] Tandel | Life cycle progression and sexual development of the apicomplexan parasite Cryptosporidium parvum[END_REF]. All images have been manually annotated by a parasitologist to delimit the outline of each parasite (as shown in Fig 4 .10). Table 4.1 indicates the size of the training/validations sets and image resolutions exploited during each step of our coarse-to-fine segmentation training process. A few data augmentation techniques have been implemented for the coarse segmentation only. The 3 scenarios are as follows.

Scenario1: Spatial transformation based data augmentation. In this case, we performed rotation, vertical flip and horizontal flip on both the original images and the associated masks.

Experimental results

Coarse segmentation

As illustrated in Fig 4 .11, the coarse segmentation step that outputs the contours of each parasite, whether it exists in individual or grouped forms. We compared our approach with five other approaches, including three deep learning approaches (U-Net_VGG19, U-Net_ResNet34 and Swin-Unet [START_REF] Hu | Swin-unet: Unet-like pure transformer for medical image segmentation[END_REF]) and two conventional approaches (watershed algorithm [START_REF] Najman | Watershed of a continuous function[END_REF] and HSV color filter). For deep learning approaches, the same scenarios as our approach were applied. First, all models were trained based on original data (without our proposed scenarios 1, 2, and 3). Fig 4 .12 illustrates the AP (Average Precision) curves for six architectures computed on the validation set of the coarse segmentation (data augmentation under sce-nario1 in the training, with the best performance for all methods relative to the other augmentation scenarios and the base dataset). AP is defined as AP=TP / (TP + FP + FN), where TP is true positive, FP is false positive, FN is false negative in term of parasite object compared to the ground truth. We compared our model (CNN combined with ViT blocks) with several CNN classifiers from the state of the art. Table 4.2 displays the performance of the different models. Our model successfully classified all the validation data, which is identical to VGG16. Nevertheless, as indicated in the table, the model VGG16 is 19 times larger than our model (134M vs. 7M).

The confusion matrix on the validation set illustrates that our model has performed perfectly on the classification of the parasite shape. From the Similar to the coarse segmentation, we compared our model with three deep learning approaches. We followed also the same training protocol along with the evaluation metric (Average Precision). It can be seen also in the metrics of our parasite fine segmentation model obtained over the validation set (illustrated in Fig 4.18). When the IoU Threshold is no more than 0.8, all the TP are successfully detected and there is no FN and FP. The number of FN and FP will both equal to 2 when the IoU threshold is 0.9.

Direct segmentation vs Coarse-to-fine segmentation

The purpose of this section is to illustrate the contribution of our coarse-tofine segmentation method compared to the standard method that performs segmentation directly. Based on aboved steps, we reconstructed the final binary mask to evaluate our coarse-to-fine segmentation framework. First we performed post-processing on the mask obtained by coarse segmentation. Comparing with the ground truth, we found that the predicted mask detected some parasites on the edges of the image, however, the ground truth has only a very small number of parasites. To reduce the number of false positives, we manually removed the parasites detected at the edges in the predicted mask. In addition, to prevent the detection of individual group of pixels with small areas that could be considered as parasites, we added a filter to detect them and then filled them with black. After all post-processing steps, we found 11 outliers from 7 validation images.

As can be seen from the Similar to the parasite shape classification experiments, we also compared our classification model with the 3 other CNN methods. As can be seen from the table 4.3, the accuracy of all models exceeds 92%, while our model outperforms other models, with 95.5% as accuracy.

The confusion matrix on the validation set illustrates that our model has well performed on all 4 classes (as displayed in Fig 4.22). For the oocyst and free form, the accuracy is much better than the other two parasite species, which is 97% and 99% respectively. While for the trophozoite and meront, both accuracy are 93%, which shows a little bit of a gap compared to the other two species. This is because a few images may be confused between them.

Conclusion

This chapter has presented a deep learning-based framework to automatically analyze and diagnose the Cryptosporidium parvum infection. The framework allows to segment precisely a group of parasites from microscopic images by employing a coarse-to-fine segmentation approach, followed by a subsequent classifier with high discriminatory power to distinguish the parasite life stages among 4 asexual stages: oocyst, trophozoite, meront, and free form. Our coarse-to-fine segmentation module has achieved an average precision of 73.45% on our validation dataset and permits to obtain over 5% of average precision in comparison with the direct segmentation. Additionally, for the parasite life stage classification module, our model also outperformed better than other state-of-the-art models, with 95.5% of accuracy.

Chapter 5

Conclusion and future work

Summary

The thesis focused on the development of new deep learning based methods for the analysis of two parasites namely Plasmodium and Cryptosporidium from microscopic images. In this sense, we proposed two major contributions.

For the Plasmodium parasite analysis, we have proposed a generic framework that permits to efficiently segment the parasite and to differentiate between four of its species: P. Falciparum, P. Ovale, P. Malaria and P. Vivax. Indeed, the global system reached 100% of diagnosis accuracy on a test set of images representing 17 infected patients. We believe that our segmentation module can be exploited as pre-segmentation tool by the scientific community to speed up the labeling of more data sources.

For the Cryptosporidium parasite analysis, we have proposed a framework that permits to precisely segment a population of parasites and classify their life stages into 4 asexual stages: oocyst, trophozoite, meront and free form. More specifically, our coarse-to-fine segmentation approach has permitted to achieve an AP (Average Precision) of 73.45% on our validation dataset, leading to an improvement of 5% compared to the top 1 stateof-the-art method. In addition, for the parasite life stage classification task, our model also outperformed better than other state-of-the-art models, with 95.5% of accuracy.

Data enrichment and model optimization

For the Plasmodium parasite analysis, our study involved four morphologies of its species: P. Falciparum, P. Ovale, P. Malaria and P. Vivax. As illustrated in Fig 5 .1, there exists a fifth species named P. Knowlesi which also cause infection within human and may threaten their lives. Future work could incorporate this species in the parasite analysis process. Moreover, life stages of the five species should also be integrated to have a finer and more precise analysis tool. Similarly, as for the Malaria framework, our Cryptosporidium framework can be optimized to offer a complete analysis tool by integrating the sexual life stages (microgamont and macrogamont as shown in Fig 5 .2) in addition to the asexual ones (oocyst, trophozoite, meront, and free form). Nevertheless, it is clear that to develop such tools, more data are required with particularly finer annotations. In this sense, our proposed frameworks can be employed as pre-segmentation tools for the new data to accelerate the annotation process. However, as illustrated in Fig 5 .3, it is difficult for biologists to identify parasites using this raw format. Therefore, artificial intelligence (AI) technology could be exploited to face issue [START_REF] Xing | Deep learning in microscopy image analysis: A survey[END_REF]. Future work for analyzing this type of image can be carried out in two successive steps: The first one is to identify whether the BF image presents an infection or not. In the positive case, the second step is to provide a coarse quantification of that infection. For this second step and as shown in Fig 5.4, the deep learning model proceeds by subdividing into small square crops the input BF image and then detect possible presence of the parasite into each crop. By this way, the AI is able to calculate the rate of infection by the parasite.

4 . 1 5 . 1

 4151 3.2 U-Net architecture [87]. . . . . . . . . . . . . . . . . . . . 3.3 AlexNet architecture [50]. . . . . . . . . . . . . . . . . . 3.4 Two versions of VGGNet, VGG16 and VGG19 [89]. . . . 3.5 Inception module [93]. . . . . . . . . . . . . . . . . . . . 3.6 Residual-Block used for ResNet architecture [44]. . . . . . 3.7 ResNet-18 architecture [51]. . . . . . . . . . . . . . . . . 3.8 Overview of our proposed Malaria diagnosis assistance system. . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 Our proposed CNN classification architecture named Light-Net. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii 3.10 Principle of the LPT S technique by horizontal and/or vertical displacements of the rigid frame f around the parasite P ∈ I 1×P varying a, b, c, d parameters from an initial position p 0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.11 Image examples from the experimented public datasets. . . 3.12 The global visualization of six datasets by the distribution of points from 200 image samples of each dataset. . . . . . 3.13 Data split for segmentation module. . . . . . . . . . . . . 3.14 Average Precision curves of specialized and generalized models obtained over the test set from Dataset 1 (left side) and the test set from Dataset 2 (right side). . . . . . . . . . 3.15 Parasite segmentation results of Generalized model. For each dataset, ground-truth on top row, automatic segmentation on bottom row. . . . . . . . . . . . . . . . . . . . . 3.16 From left to right, parasite detection metrics of the Generalized segmentation model on test sets from Dataset 1 on the left and Dataset 2 on the right. . . . . . . . . . . . . . . 3.17 For each dataset, ground-truth segmentation on the left vs automatic segmentation on the right: illustration of some missed parasites shown in orange and merged ones shown in yellow. . . . . . . . . . . . . . . . . . . . . . . . . . . 3.18 Performance comparison between generalized parasite segmentation models based on 3 deep architectures on a merged test set from Dataset 1 and Dataset 2 . . . . . . . . . . . . . 3.19 Parasite segmentation results from our generalized model (colored images) vs Abbas and Dijkstra method [3] (quadtone masks from CDC blind data; yellow pixels: TP, green pixels: FP, red pixels: FN, black: background). . . . . . . 3.20 An output example of our LPTS parasite augmentation technique. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.21 From left to right, confusion matrices obtained on the parasite species test set from Dataset 1 by Light-Net and VGG16 [89] respectively. . . . . . . . . . . . . . . . . . . . . . . . . .xiii 3.22 Parasite segmentation results of our system over samples of Dataset 3 , Dataset 5 and Dataset 6 (from left to right). . . Cryptosporidium analysis by biologist expert. . . . . . . . 4.2 Huge challenges present in detecting parasite contours, observed from the labelings of experts. Different situation as follows: (a) Confusing contours, (b) Irregular shapes and (c) Different brightness . . . . . . . . . . . . . . . . . . . 4.3 The Vision Transformer (ViT) framework [28]. . . . . . . 4.4 Architecture TransUNet[12]. . . . . . . . . . . . . . . . . 4.5 Architecture Swin-Unet [9]. . . . . . . . . . . . . . . . . 4.6 An overview of our method, which consists of a coarseto-fine segmentation module, a loop control module and a module of parasite life stage classifier. . . . . . . . . . . . 4.7 Our implemented segmentation architecture TransUNet. . 4.8 Our proposed classification architecture TransCNN. . . . . 4.9 Parasite fine segmentation for grouped parasites. . . . . . . 4.10 Example of parasite image annotated by biologist using 4 different colors. . . . . . . . . . . . . . . . . . . . . . . . 4.11 The coarse segmentation step. . . . . . . . . . . . . . . . 4.12 AP curves on the validation set of the coarse segmentation. 4.13 Parasite detection metrics of the our parasite coarse segmentation model on the validation set. . . . . . . . . . . . 4.14 The shape classification step. . . . . . . . . . . . . . . . . 4.15 Confusion matrices obtained by our model on the parasite shape validation set. . . . . . . . . . . . . . . . . . . . . . 4.16 The fine segmentation step. . . . . . . . . . . . . . . . . . 4.17 AP curves computed on the validation set of fine segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.18 Parasite detection metrics of our parasite fine segmentation model obtained over the validation set. . . . . . . . . . . . 4.19 AP curves over the validation set of coarse-to-fine segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv 4.20 Parasite detection metrics of our parasite coarse-to-fine segmentation model on validation set. . . . . . . . . . . . . . 4.21 The parasite life stage classification step. . . . . . . . . . . 4.22 Confusion matrices obtained by our model on the parasite life stage validation set. . . . . . . . . . . . . . . . . . . . Malaria parasite species and its life stage (extracted from [81]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Cryptosporidium parasite life stages (extracted from [41]). 5.3 An example of BF microscopic image of Cryptosporidium parasite. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Deep learning method for parasite detection and quantification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 AI framework for BF image analysis. . . . . . . . . . . . xv List of Tables

FIGURE 1 . 1 :

 11 FIGURE 1.1: Malaria world map 2022 (extracted from WANDA).

FIGURE 1 . 2 : 4 Chapter

 124 FIGURE 1.2: Cryptosporidium parvum life cycle (extracted from CDC).

Fig 2 . 1

 21 shows the life cycles of the Plasmodium parasite.

  Fig 2.2 and Fig 2.

  3.

FIGURE 2 . 2 :

 22 FIGURE 2.2: Detailed information of the Cryptosporidium parvum life stages.

FIGURE 2 . 3 :

 23 FIGURE 2.3: Cryptosporidium parvum life cycle examples.

FIGURE 2 . 4 :

 24 FIGURE 2.4: Outline of the Cryptosporidium parvum life stages (extracted from [8]).

Fig 2. 5

 5 shows some samples of parasite species addressed in[START_REF] Yoselyn | Data Augmentation using Generative Adversarial Network for Gastrointestinal Parasite Microscopy Image Classification[END_REF].

FIGURE 2 . 5 :

 25 FIGURE 2.5: A sample of each parasite species addressed in [63].

  Fig 3.1 (Row

Chapter 3 .

 3 FIGURE 3.2: U-Net architecture [87].

  Fig 3.5. Each inception block contains convolutional layers of different kernel sizes to extract features in different scales.

FIGURE 3 . 3 :

 33 FIGURE 3.3: AlexNet architecture [50].

FIGURE 3 . 4 :

 34 FIGURE 3.4: Two versions of VGGNet, VGG16 and VGG19 [89].

FIGURE 3 . 5 :

 35 FIGURE 3.5: Inception module [93].

Chapter 3 .

 3 FIGURE 3.6: Residual-Block used for ResNet architecture [44].

Chapter 3 .

 3 FIGURE 3.8: Overview of our proposed Malaria diagnosis assistance system.

FIGURE 3 . 9 :

 39 FIGURE 3.9: Our proposed CNN classification architecture named Light-Net.

3. 3 . 3 . 8 )FIGURE 3 . 10 :

 338310 FIGURE 3.10: Principle of the LPT S technique by horizontal and/or vertical displacements of the rigid frame f around the parasite P ∈ I 1×P varying a, b, c, d parameters from an initial position p 0 .

Chapter 3 .

 3 FIGURE 3.11: Image examples from the experimented public datasets.

Figure 3 . 4 FIGURE 3 . 12 :

 34312 Figure 3.12 visualizes the six datasets by data dimensionality reduction. In particular, PCA (Principal Component Analysis) and t-SNE (tdistributed stochastic neighbor embedding) [62] were applied to each dataset to generate a point distribution from the 200 image samples. This figure allows us to observe the intra-and inter-class relationships of the images in the different datasets. It is worth noting that the visual exploration shows a high intra-class homogeneity for the Datasets 3,4 ; while the others are more moderate. The Dataset 4 is isolated; it confirms the specificity of its images, as they involve single cells, while the other datasets consist of multicellular images. The Datasets 2,3,5,6 are connected by overlapping regions. In these datasets, the regions of Datasets 2,6 are different from the other datasets because both are homogeneously stretched and relatively overlapping. Despite Dataset 1 represents multicellular images, it demonstrates a large inter-class gap compared to the other datasets. These 6 datasets are summarized inTable 3.1. The table illustrates that only Dataset 1 and Dataset 2 propose the groundtruth for parasite segmentation. Furthermore, only Dataset 1 includes 4

Chapter 3 .Figure 3 .

 33 Figure 3.13 illustrates how we split the training / validation / test sets to experiment with Dataset 1 and Dataset 2 .The images in each folder are chosen randomly. Nevertheless, each set in Dataset 1 included the images for each parasite species, which is an essential condition for learning its shape segmentation. As displayed in Figure3.13, two patients (with Falciparum species) were excluded from the validation set of Dataset 1 because they both had only three images. Therefore, we selected one image for each of them as the test set and added the remaining two images to the training set.

FIGURE 3 . 13 :

 313 FIGURE 3.13: Data split for segmentation module.

Figure 3 .

 3 FIGURE 3.14: Average Precision curves of specialized and generalized models obtained over the test set from Dataset 1 (left side) and the test set from Dataset 2 (right side).

Figure 3 .

 3 18 illustrates the AP curves of the three architectures computed on a test set corresponding to the merging of the test sets from Dataset 1 and Chapter 3. Generalized deep learning for Plasmodium parasite image analysis (A) Dataset 1 test set: from left to right image examples representing Falciparum, Malariae, Ovale and Vivax species. (B) Dataset 2 test set: image examples of Falciparum species.

FIGURE 3 . 15 :

 315 FIGURE 3.15: Parasite segmentation results of Generalized model. For each dataset, ground-truth on top row, automatic segmentation on bottom row.

FIGURE 3 . 16 :(A) Dataset 1 (B) Dataset 2 FIGURE 3 . 17 :

 31612317 FIGURE 3.16: From left to right, parasite detection metrics of the Generalized segmentation model on test sets from Dataset 1 on the left and Dataset 2 on the right.

FIGURE 3 . 18 :

 318 FIGURE 3.18: Performance comparison between generalized parasite segmentation models based on 3 deep architectures on a merged test set from Dataset 1 and Dataset 2 .

FIGURE 3 . 19 :

 319 FIGURE 3.19: Parasite segmentation results from our generalized model (colored images) vs Abbas and Dijkstra method [3] (quadtone masks from CDC blind data; yellow pixels: TP, green pixels: FP, red pixels: FN, black: background).

3. 4 . 3

 43 Results and evaluation of the generalized classifica-

  (A) Original image. (B) Ground-truth. (C) Generated mask crops. (D) Original crops.

FIGURE 3 . 20 :

 320 FIGURE 3.20: An output example of our LPTS parasite augmentation technique.

FIGURE 3 . 21 :

 321 FIGURE 3.21: From left to right, confusion matrices obtained on the parasite species test set from Dataset 1 by Light-Net and VGG16 [89] respectively.

3. 4 . 4 . 1

 441 Patient level evaluation

FIGURE 3 . 22 :

 322 FIGURE 3.22: Parasite segmentation results of our system over samples of Dataset 3 , Dataset 5 and Dataset 6 (from left to right).

FIGURE 4 . 1 :

 41 FIGURE 4.1: Cryptosporidium analysis by biologist expert.

FIGURE 4 . 2 :

 42 FIGURE 4.2: Huge challenges present in detecting parasite contours, observed from the labelings of experts. Different situation as follows: (a) Confusing contours, (b) Irregular shapes and (c) Different brightness

4. 2 .

 2 Exploited Transformer-based architectures 57 In the last decade, deep learning-based networks have been broadly employed for image segmentation in the medical field and have demonstrated high performance. However, they suffer from a lack of understanding of long-range dependencies in images owing to the induction bias inherent among convolutional architectures. To address this limitation, we have discovered that a growing number of recent studies have been proposed to encode long-range dependencies based on Transformer architectures exploiting self-attention mechanism over the state-of-the-art [112, 114, 108, 107]. Dosovitskiy et al.[28] first proposed the concept of Vision Transformer (ViT), that is a pure transformer for direct application to image classification task. ViT follows the original transformer as much as possible in its design, as shown in Fig 4.3. The authors split the image into patches with fixed-size, added position embeddings and then fed these vector sequences to the Transformer encoder.

FIGURE 4 . 3 :

 43 FIGURE 4.3: The Vision Transformer (ViT) framework [28].

Fig 4 . 4 ,

 44 TransUNet adopts a hybrid CNN-Transformer architecture, in which feature maps are first generated by

FIGURE 4 . 5 :

 45 FIGURE 4.5: Architecture Swin-Unet [9].

FIGURE 4 . 6 :

 46 FIGURE 4.6: An overview of our method, which consists of a coarse-to-fine segmentation module, a loop control module and a module of parasite life stage classifier.

FIGURE 4 . 7 :

 47 FIGURE 4.7: Our implemented segmentation architecture TransUNet.

FIGURE 4 . 8 :

 48 FIGURE 4.8: Our proposed classification architecture TransCNN.

FIGURE 4 . 9 :

 49 FIGURE 4.9: Parasite fine segmentation for grouped parasites.

FIGURE 4 . 10 :FIGURE 4 . 11 :

 410411 FIGURE 4.10: Example of parasite image annotated by biologist using 4 different colors.

FIGURE 4 .

 4 FIGURE 4.12: AP curves on the validation set of the coarse segmentation.

FIGURE 4 . 13 :

 413 FIGURE 4.13: Parasite detection metrics of the our parasite coarse segmentation model on the validation set.

FIGURE 4 . 14 :

 414 FIGURE 4.14: The shape classification step.

  Fig 4.15, our model succeeds to classify all the validation data.

FIGURE 4 . 16 :

 416 FIGURE 4.16: The fine segmentation step.

Fig 4 .

 4 Fig 4.[START_REF] Chollet | Xception: Deep learning with depthwise separable convolutions[END_REF] shows the fine segmentation step that permits to isolate each parasite from the grouped ones.Similar to the coarse segmentation, we compared our model with three deep learning approaches. We followed also the same training protocol along with the evaluation metric (Average Precision). Fig 4.17 demonstrates the AP curves computed on the validation set for our fine segmentation model. The curves indicate that our model gives a better performance than the other models. It is notable that even with a high IoU threshold, the AP value of our model maintains the best AP value amongst the other models, which reveals a high accuracy in parasite separation.It can be seen also in the metrics of our parasite fine segmentation model obtained over the validation set (illustrated inFig 4.18). When the IoU Threshold is no more than 0.8, all the TP are successfully detected and there is no FN and FP. The number of FN and FP will both equal to 2 when the IoU threshold is 0.9.

FIGURE 4 .

 4 FIGURE 4.17: AP curves computed on the validation set of fine segmentation.

FIGURE 4 . 18 :

 418 FIGURE 4.18: Parasite detection metrics of our parasite fine segmentation model obtained over the validation set.

FIGURE 4 . 19 :

 419 FIGURE 4.19: AP curves over the validation set of coarse-to-fine segmentation.

  Fig 4.19, with an IoU threshold of 0.5, our coarse-to-fine model achieved an AP of 73.45%, exceeding all comparative segmentation approaches. Furthermore, the model allows to obtain over 5% of AP in comparison with the coarse model. While in Fig 4.20, compared with the result in Fig 4.13, the number of both FN and FP has decreased to less than 100 (IoU threshold=0.5).

FIGURE 4 . 20 :

 420 FIGURE 4.20: Parasite detection metrics of our parasite coarse-to-fine segmentation model on validation set.

FIGURE 4 . 21 :

 421 FIGURE 4.21: The parasite life stage classification step.

FIGURE 5 . 1 :

 51 FIGURE 5.1: Malaria parasite species and its life stage (extracted from [81]).

5. 2

 2 Perspective for improvement of parasite analysis 5.2.

FIGURE 5 . 2 :

 52 FIGURE 5.2: Cryptosporidium parasite life stages(extracted from[START_REF] Gunasekera | Organoids and bioengineered intestinal models: potential solutions to the Cryptosporidium culturing dilemma[END_REF]).

FIGURE 5 . 3 :

 53 FIGURE 5.3: An example of BF microscopic image of Cryptosporidium parasite.

5. 2 . 2

 22 Exploitation of a new form of data for Cryptosporidium: bright-field microscopic images Context and motivations -During this thesis, we mainly exploited the fluorescence microscopic image for the analysis of the Cryptosporidium parasite. Nevertheless, there exists a raw data named Bright-Field (BF) image (without fluorescent markers) that is often used in the study of Cryptosporidium parasites. Indeed, the raw format permits to study the entire life cycle (asexual and sexual) of the parasites without killing the experimented cell cultures following the usage of the fluorescent marker.

FIGURE 5 . 4 :

 54 FIGURE 5.4: Deep learning method for parasite detection and quantification.

FIGURE 5 . 5 :

 55 FIGURE 5.5: AI framework for BF image analysis.

  Plasmodium parasite image analysis . . . . . . . . . . . . 7 2.3 Cryptosporidium parasite image analysis . . . . . . . . . . 2.4 Other parasite image analysis . . . . . . . . . . . . . . . . 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 3 Generalized deep learning for Plasmodium parasite image analysis 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Exploited CNN-based architectures . . . . . . . . . . . . 3.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Parasite segmentation . . . . . . . . . . . . . . . . 3.3.2 Parasite classification . . . . . . . . . . . . . . . . Experimental study . . . . . . . . . . . . . . . . . . . . . 3.4.1 Datasets analysis . . . . . . . . . . . . . . . . . . 3.4.2 Results and evaluation of the generalized segmentation module . . . . . . . . . . . . . . . . . . . . 3.4.2.1 Data preparation . . . . . . . . . . . . . 3.4.2.2 Segmentation performance analysis . . . 3.4.3 Results and evaluation of the generalized classification module . . . . . . . . . . . . . . . . . . . . 3.4.3.1 Data preparation . . . . . . . . . . . . . 3.4.3.2 Classification performance analysis . . . 3.4.4 Global system results and evaluation . . . . . . . . 3.4.4.1 Patient level evaluation . . . . . . . . . 3.4.4.2 Performance analysis on other datasets with partial ground-truth . . . . . . . . . 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . .3.2 Loop control module . . . . . . . . . . . . . . . . 4.3.3 Parasite life stage classification module . . . . . . 4.4 Experimental study . . . . . . . . . . . . . . . . . . . . . 4.4.1 Data preparation . . . . . . . . . . . . . . . . . . 4.4.2 Experimental results . . . . . . . . . . . . . . . . 4.4.2.1 Coarse segmentation . . . . . . . . . . . 4.4.2.2 Parasite shape classification . . . . . . . 4.4.2.3 Fine segmentation . . . . . . . . . . . . x 4.4.2.4 Direct segmentation vs Coarse-to-fine segmentation . . . . . . . . . . . . . . . . 4.4.2.5 Parasite life stage classification . . . . . 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion and future work 5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Perspective for improvement of parasite analysis . . . . . 5.2.1 Data enrichment and model optimization . . . . . 5.2.2 Exploitation of a new form of data for Cryptosporidium: bright-field microscopic images . . . . . . . Cryptosporidium parvum life cycle (extracted from CDC). Cryptosporidium parvum life cycle examples. . . . . . . . 2.4 Outline of the Cryptosporidium parvum life stages (ex-

	xi
	List of Figures
	1.1 Malaria world map 2022 (extracted from WANDA). . . . .
	1.2
	Bibliography

1 Introduction 1 1.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 State of the art of parasite image analysis 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 3.3.3 Proposed parasite-oriented data augmentation technique . . . . . . . . . . . . . . . . . . . . . . . . ix 3.4 4 Granularity guided deep learning for precise Cryptosporidium parasite image analysis 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Exploited Transformer-based architectures . . . . . . . . . 4.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Coarse-to-fine segmentation module . . . . . . . . 4.3.1.1 Coarse segmentation . . . . . . . . . . . 4.3.1.2 Parasite shape classifier after cropping . 4.3.1.3 Fine segmentation . . . . . . . . . . . . 42.1 The life cycles of the Plasmodium parasite (extracted from [72]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Detailed information of the Cryptosporidium parvum life stages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3

  3.1 Characteristics of the public malaria datasets used in our experiments. . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Quantity of parasite images using a cascade of augmentations with species balancing. . . . . . . . . . . . . . . . . 3.3 Classifiers performance comparison on the Crops LPTS + standard augmentation validation set using standard metrics 3.4 Classification results obtained by the two architectures on a test set of 5889 Falciparum crops from Dataset 2 . . . . . 3.5 Results of the global diagnosis system at the patient level. .

3.6 Global system performance in term of parasite detection and species classification. . . . . . . . . . . . . . . . . . . 4.1 Detailed information of dataset used in different steps . . . 4.2 Comparison of the performance and the number of parameters between our classification architecture and some CNN architectures . . . . . . . . . . . . . . . . . . . . . 4.3 Comparison of the performance between our classification model and 3 other CNN methods . . . . . . . . . . . . . .

  Chapter 3. Generalized deep learning for Plasmodium parasite image analysisJi represents Jaccard index and is defined as:

	Ji(bm a , bm gt ) =	bm a ∩ bm gt bm a ∪ bm gt	with	(3.3)

bm a , bm gt ) (3.2)

TABLE 3 .

 3 of parasites. For these reasons, we used these two datasets as basis for the training / validation / test of our generalized segmentation and classification modules and we utilized Datasets 3,6 for additional tests.

	Dataset	Infected/non-infected	Type of	Total of	Image resolution	Total of	Parasite	Parasite
	reference	patients	image*	images	(W x H)	parasites	species**	segmentation truth
	Dataset 1 [59]	17/0	PC	210	2592 x 1944	1437	F,M,O,V	Yes
	Dataset 2 [2]	17/0	PC	883	1382 x 1030	3586	F	Yes
	Dataset 3 [21]	5/0	PC	331	2400 x 1800	-	NA	No
	Dataset 4 [82]	151/50	IC	27 558	Heterogeneous	13 779	F	No
	Dataset 5 [96]	-	PC	654	640 x 480	-	F	No
	Dataset 6 [57]	-	PC	1328	1944 x 1328	-	V	No

1: Characteristics of the public malaria datasets used in our experiments. *PC: Population of Cells, IC: Individual Cell. **F: Falciparum, M: Malariae, O: Ovale, V: Vivax, NA: Not Available.

species

TABLE 3 .

 3 2: Quantity of parasite images using a cascade of augmentations with species balancing.

	Parasite	Global image dataset 1 Crops LPTS augmentation* Crops LPTS + standard augmentation
	species	Train Validation Test Train Validation	Test	Train	Validation
	Falciparum	74	14	16 7363	1333	1524 11289	2047
	Malariae	27	5	5	297	45	45	10692	2075
	Ovale	21	4	4	261	32	36	11000	2052
	Vivax	32	4	4	441	54	81	11178	2044

*11512 generated crops using Eq. 3.7 over the 1437 parasites of Dataset 1 (close to maximal value 12933 provided by Eq. 3.8).

TABLE 3 .

 3

			3: Classifiers performance comparison on
		the Crops LPTS + standard augmentation validation
			set using standard metrics
	Parasite		Accuracy			Precision			Recall
	species	VGG16 Light-Net* Light-Net VGG16 Light-Net* Light-Net VGG16 Light-Net* Light-Net
	Falciparum	0.96	0.65	0.98	0.95	0.89	0.98	0.96	0.65	0.98
	Malariae	0.99	0.94	0.99	0.96	0.85	0.98	0.99	0.94	0.99
	Ovale	0.72	0.47	0.83	0.77	0.53	0.84	0.72	0.47	0.83
	Vivax	0.77	0.72	0.84	0.76	0.57	0.83	0.77	0.72	0.84
	VGG16 applied from [89].*Trained on reduced training set (without 8 positions of LPTS
	crops).

TABLE 3 .

 3 4: Classification results obtained by the two architectures on a test set of 5889 Falciparum crops from Dataset 2 .

	Classifier	True Positive Falciparum Malariae Ovale Vivax False Negative
	VGG16 [89]	5798	32	19	40
	Light-Net	5864	5	1	19

TABLE 3 .

 3

		5: Results of the global diagnosis system at
			the patient level.
	Dataset		Total of images	Total of parasites GT** Predicted GT** Predicted Species*
		Patient 1	2	55	56	F	F
	Dataset 1 [59]	Patient 2 Patient 3 Patient 4	2 2 2	33 2 2	42 2 2	F M O	F M O
		Patient 5	2	3	5	V	V
		Patient 1	8	24	29	F	F
	Dataset 2 [2]	Patient 2 Patient 3 Patient 4	6 22 8	28 48 29	27 44 33	F F F	F F F
		Patient 5	6	19	24	F	F
	*F: Falciparum, M: Malariae, O: Ovale, V: Vivax. **GT: Ground-Truth.

TABLE 3 .

 3 6: Global system performance in term of parasite detection and species classification.

	Test dataset	Infected patients	Total of images	Parasite detection rate	Predicted patients	Ground-truth species	Species classification
	Dataset 3 [21]	5	331	0.99	5/5	-	-
	Dataset 4 [82]	151	13779	-	-	Falciparum	0.63
	Dataset 5 [96]	-	654	0.99	-	Falciparum	0.84
	Dataset 6 [57]	-	1328	0.85	-	Vivax	0.31

TABLE 4 . 2 :

 42 Comparison of the performance and the number of parameters between our classification architecture and some CNN architectures

	4.4. Experimental study

FIGURE 4.15: Confusion matrices obtained by our model on the parasite shape validation set.

TABLE 4 . 3 :

 43 Comparison of the performance between our classification model and 3 other CNN methods

	Model	Accuracy Precision Recall
	VGG16	0.946	0.947	0.946
	ResNet50	0.923	0.923	0.923
	Simple CNN (6 convolution layers) 0.953	0.953	0.953
	Our model	0.955	0.955	0.955

FIGURE 4.22: Confusion matrices obtained by our model on the parasite life stage validation set.

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Acknowledgements

, I would like to thank european Interreg program for funding my thesis research at the University of Lille.

Chapter 4. Granularity guided deep learning for precise Cryptosporidium parasite image analysis CNN and then patch embedding is performed based on these feature maps. The authors believe that Transformer can be leveraged as a powerful encoder for image segmentation, combining with U-Net for the enhancement of finer details by recovering local spatial information. 6 illustrates the overview of our framework, which consists of a coarse-to-fine segmentation module, a loop control module and a module of parasite life stage classifier. Our coarse-to-fine segmentation method applies deep learning models by three consecutive steps of analysis: 1) a coarse segmentation model which inputs a microscopic image and outputs a mask with delimited contours that delineate individual or grouped

Methodology