Laurent Dupont
email: l.dupont@univ-lorraine.fr

Fedoua Kasmi
email: fedoua.kasmi@univ-lorraine.fr

Joshua M Pearce
email: joshua.pearce@gmx.com

Roland J Ortt
email: j.r.ortt@tudelft.nl

Title of the issue: "Do-It-Together" and Alternative Innovations Focus: "Do-It-Together" and Innovation: Transforming European Industry 1

This Special Issue on "Do It Together and Innovation" brings together a set of expertise, disciplines, and networks to address the environmental and socio-economic challenges facing our current industrial model. The accelerated development of advanced technologies such as the Internet of Things (IoT), 3D printing, immersive technologies, Peer-to-Peer (P2P), the interconnection between digital and physical environments, and the spread of alternative development models such as the circular economy offer the potential for a transition to new innovative and sustainable hybrid modes of innovation, production and consumption.

In this context, in 2018-2019 the European Commission 2 initiated a call for projects on Open Innovation for collaborative production engineering aligned with the strategy of Factories of the Future (European Commission Directorate-General for Research and Innovation, 2013), with the ambition of transferring the success of the Do-It-Yourself (DIY) phenomenon to Small and medium-sized enterprises (SMEs). Indeed, the emergence and multiplication of spaces and networks of makers, hackerspaces, micro-factories, FabLabs or other spaces equipped with digital manufacturing tools and technologies has favored new types of innovation processes and the development of a more agile, democratized, and distributed production based on commons-based peer production, particularly digital

The richness of the concepts solicited in this call for projects is reflected in the launch in 2019 of four European projects 2 addressing the following subjects: Open Innovation Digital Platform and Fablabs for Collaborative Design and Production of personalized/customized FMCG (DIY4U), open INnovation Ecosystems for Do It Together process (INEDIT), Social Manufacturing Framework for Streamlined Multi-stakeholder Open Innovation Missions in Consumer Goods Sectors (iPRODUCE), and Company-Community Collaboration for Open-Source Development of products and services (OPEN_NEXT). The diversity of the actors, industrial sectors, technologies and territories involved in these projects provides complementary perimeters of action and experimentations [START_REF] Dupont | Open innovation, technologies & communities as enablers of socio-economical transition[END_REF].

The feedback from these sister projects highlights the relevance of approaching this theme in the most transversal way possible by relying on the interdisciplinarity inherent to engineering, technology, and innovation management, as proposed by the articles which comprise this special issue. Nevertheless, according to the European Factories of the Future Research Association (EFFRA), these projects are only a small part of a very large public-private partnership (PPP) for research and innovation in advanced manufacturing. The EFFRA points out that with €1.15 billion, "Factories of the Future" is the European Union's main program to achieve the next industrial revolution: materializing Factories 4.03 . The Factories of the Future (FOF) concept is therefore driven and supported by the industrial and manufacturing communities. It is part of the Fourth Industrial Revolution, also referred to as Industry 4.0. According to Ortt et al., (2020) "Industry 4.0 is a concept that requires integration of technological, business, and organizational aspects, several dimensions are formed in these models." Furthermore, implementation of Industry 4.0 differs per sector and type of company. This is even more true for SMEs, which do not always have the possibility of deploying robust and standardized processes, especially if they are not directly transferable between sectors. That means that SMEs because of their way of working have more problems with adopting and implementing Industry 4.0. In this sense, it is as if these SMEs had to abandon the DIY approach in order to enter the Industry 4.0 era by themselves.

On the other hand, society is changing and awareness of the negative impact of technological advances on our natural ecosystem seems to be increasing. Some authors have argued for a Quadruple or even Quintuple Helix of innovation [START_REF] Carayannis | The Quadruple/Quintuple Innovation Helixes and Smart Specialization Strategies for Sustainable and Inclusive Growth in Europe and Beyond[END_REF], aiming to integrate citizens and representatives of the biosphere into the well-established PPP innovation production process.

Although many of the expected impacts of the European call for projects are ambitious (Establish Open-Innovation networks for manufacturing that support customer-driven production; Creation of specific business models for the engineering of customized solutions, particularly for SMEs, rapid demand changes and shorter time to market; Improvement of the co-design and co-development capabilities toward a reduction of development costs of new products and services; Increase of product variety and personalization for higher customer satisfaction and loyalty) 1 , we still need to support SMEs to raise their awareness of environmental issues and their ability to act.

Our factories and companies are part of a whole. Through their economic and social activities, they play a major role in the organization of territories and the impact on the natural ecosystem. If the effects induced by the large industrial groups are quickly identifiable by the volumes they generate, the negative externalities of the SMEs (their supply chain and the industrial sector in which they are included) are sometimes more difficult to qualify.

The FOF actors have an urgent need to put into perspective the impacts generated by Cyber-Physical Systems regarding environmental and even geopolitical issues, which we can no longer ignore. The FOF must minimize or even eliminate all negative externalities for its entire value chain. Human and economic activities can be rethought through the emergence and implementation of FOF. For this, it seems wise to combine it with other concepts such as the circular economy or industrial ecology [START_REF] Dupont | Towards the Factories of the Future[END_REF].

Derived from Do-It-Yourself, Do-It-Together (DIT) is an alternative design process that enables customized global design, promoting local manufacturing closer to consumers who actively contribute to their production. Value co-creation brings benefits to all stakeholders involved in DIT processes by engaging a community of customers, professionals and producers from the co-creation of product/service ideas to custom production. The DIT concept as a form of participatory design and collaborative production strategy [START_REF] Dupont | Towards the Factories of the Future[END_REF] aims to create open co-creation and manufacturing ecosystems to transform Do-It-Yourself practices into a professional approach to capitalize the knowledge, creativity, and ideas of design and engineering. This approach aims to engage the integration of individuals/customers in all physical product manufacturing processes, from ideation to production for economic, societal, and environmental purposes. This radical ambition implies that DIT promotes smallscale production at local sites, even the development of an urban production model [START_REF] Herrmann | Urban Factories: Identifying Products for Production in Cities In Technologies and Eco-innovation towards Sustainability[END_REF][START_REF] Herrmann | Urban production: State of the art and future trends for urban factories[END_REF][START_REF] Juraschek | Analysis and Development of Sustainable Urban Production Systems[END_REF] with decentralized and distributed characteristics [START_REF] Cerdas | Life Cycle Assessment of 3D Printed Products in a Distributed Manufacturing System[END_REF] as an alternative to globalized manufacturing values chains. Aiming at "design global/manufacturing local" (DGML) (Kostakis et al., 2018) seems to be a prototype of an industrialization transition that is taking place as a potential driver to propose an alternative globalization manufacturing paradigm.

We propose here to shed new light on the concept of FOF with in-depth reflections on the DIT concept from technological to territorial dimensions for a more local and sustainable European Industry.

Based on theory and practice, this special issue brings together twenty international researchers and practitioners. Researchers because the authors are part of a scientific community with a specific approach to generate new knowledge and contribute to shedding light on this protean concept. Practitioners because they are actors involved in operational European projects (INEDIT 4 or OPEN_NEXT 5 ), as part of an Innovation Action program. This means that these projects aim to develop concrete tools, methods, products, and services. Four articles explore the different facets of this concept, enriching existing definitions and, above all, proposing concrete avenues for implementing and operationalizing DIT, even outlining potential business models. To go further, three articles analyze more broadly the contribution of Open-Source Appropriate Technology (OSAT) and Peer-to-Peer (P2P) in the evolution of companies and how territories can promote the emergence of such innovative ecosystems that are more inclined to value and network local resources.

The paper of Marche, B., Kasmi, F., Mayer, F. and Dupont, L. aims to define and formalize the DIT approach for its operationalization based on an analysis of the literature on social manufacturing. DIT is presented as a new approach to social manufacturing that relies on the co-creation and open manufacturing of personalized products involving consumers, assisted by a community of professionals and experts in a cyber-manufacturing space. DIT is defined in this article as "an alternative design process which allows for open global design and openmanufacturing, promoting local production closer to manufacturers/"prosumers". It enables "prosumers" to produce and consume goods which they have helped to design. This way, they become active co-creators who could disrupt the existing local mass production industry. Cocreation of value brings benefits to all stakeholders involved in DIT processes by engaging a community of customers, professionals, and producers from the co-creation of the product/service ideas to custom production." Building on this definition, the authors propose a generic DIT process and an organizational framework to clarify its organization on an industrial scale. The generic DIT process mobilizes insights from the fields of co-creation and open manufacturing. The organizational framework is a combination of the social manufacturing model and the open manufacturing model. The former clarifies how stakeholders collaborate to design customized products while the latter describes how the exchange of services and knowledge between stakeholders is organized. In conclusion, the authors point out that the implementation of DIT in practice requires the development of an empirical application to validate the different aspects of their conceptual framework. Specific ecosystems, logistics and economic models need to be designed and implemented to scale up to industrialization. Pallot M., Fleury S., Poussard B. and Richir S. provide a systematic literature review to explore the challenges and enabling technologies for implementing DIT. The authors use Product Lifecycle Analysis (PLC) to build an analysis grid for the thirty-eight most relevant articles selected for this study. They propose seven stages from which they analyze in particular the previous empirical work described in the articles: Co-creation (Social Ideation); Co-design Open Design; Open Manufacturing; Co-Marketing (Social Marketing); Social Commercialization; Green logistics and Reverse logistics; Social Reuse, Refurbishment and Repurposing. The authors focus on identifying the challenges overcome by XR (eXtended Reality) technologies and their induced benefits, as well as the cross-cutting drawbacks to the early stages of the NPD (New Product Development) PLC. XD (eXperience Design) technologies appear to be an appropriate catalyst for NPD implementation, particularly in the ideation and design stages, due to the power of virtual prototyping, allowing stakeholders to: (i) quickly reach a mutual understanding of an idea, its associated concepts and its usage scenario; (ii) anticipate the resulting UX; hence the possibility to deduce the degree of customer satisfaction and willingness to adopt the represented solution; (iii) acquire the necessary knowledge by learning quickly on the job without any risk; (iv) follow a safe step-by-step process to accomplish a task. The authors note a significant lack of empirical studies on the negative impacts of these technologies for DIT. They do, however, identify a significant impediment related to the lack of appropriate customer skills to appropriately contribute to design and manufacturing activities.

The paper of Leiting, T., Külschbach, A. and Stich, V. provides one of the answers to the questions raised by Marche et al., 2022 by addressing sustainable business models for cocreation ecosystems. The authors seek to develop a sustainable business model for a cocreation platform in a DIT approach with a concrete application to the furniture industry. Customers and manufacturers are brought together on a digital platform -as equal partners and supported by designers and manufacturers in a co-creation process. So far, the business models of existing platforms focus either on the customer side or the production side. There are no sustainable multi-stakeholder business models that equally involve all stakeholders in the value chain to implement DIT co-creation in practice. Therefore, the objective of the paper is to develop a business model for a DIT co-creation platform based on a four-step business model innovation framework: 1) definition of the business field, 2) determination of the business elements, 3) identification of business options, 4) creation of business models. Using this method, a platform business model was developed for the DIT co-creation process of the INEDIT project. According to the authors, this platform business model design can be applied in other application scenarios and the DIT co-creation process can be established in various industries. SMEs will benefit from this model by increasing the economic viability of producing lucrative, customized products in small batches, manufactured on demand.

With a focus on operationalizing DIT, Franz, J. and Pearce, J. propose a new business model for free and open-source hardware (FOSH), specific to companies making specialized components for manufacturers using a case study of an open-source screw manufacturer. The case study explores the economics of building a system to manufacture a specialized component for other companies and prosumers working in distributed recycling and additive manufacturing (DRAM). Component payback time is calculated under various scenarios, the sales required to provide an attractive income for a small business are quantified, and the point at which business expansion is required is determined. The authors provide five avenues for small businesses to generate revenue and leverage applications of this technology in an open-source DIT model: 1) Selling screws manufactured on the open-source screw manufacturing system; 2) Selling consumables, components, kits, or complete screw machines; 3) Selling custom open-source screws; 4) Selling services revolving around the custom screws; 5) Selling advertising via trending content on various online platforms. The authors analyze the advantages and disadvantages of this new business model and indicate that to serve the growing DRAM market, over 1,000 small businesses could follow a DIT approach of sharing FOSH designs while manufacturing and profiting locally.

According to Garnier, C. and Capdevilla, I. FabLabs offer adapted responses to different emerging economic-productive trends and contribute to the transformation of traditional industry and the democratization of personal manufacturing to create a new socio-technical system of manufacturing, based on a more social approach. In their article they argue that FabLabs are social manufacturing platforms, enabling different combinations of interactions between industries and individuals, through their activities around making (producing goods from raw materials), hacking (reusing/combining produced goods) and coding (producing and reusing using digital goods). The article also defines four different modes of social fabrication depending on the technologies used (open or proprietary) and the location of the fabrication (local or industrial). It contributes to the literature on collaborative spaces by explaining prospective development scenarios in relation to new modes of production. It also complements the literature by contextualizing the physical spaces where social manufacturing takes place. In conclusion, the authors emphasize the need to compare the current implementation of the four modes of social fabrication to track the evolution of these emerging practices in future work. More empirical findings are also needed to observe and validate these practices in the field.

Hassan, M. , Mies, R. and Jochem R. highlight the significant potential of Open-Source Hardware (OSH) as a strategic source of open innovation for SMEs. They propose a Company-Community Collaboration (C3) approach as a radical innovation approach in which companies collaboratively engage within a community framework by creating joint projects with a diverse set of co-creators who follow open-source principles to create a level playing field. The authors aim to identify the key enablers that help SMEs engage in successful collaboration with OSH communities for the co-creation of physical products. For this purpose, they use a methodological approach combining a consultation of existing multidisciplinary literature and gathering expert opinions via the E-Delphi method. The results allow us to identify seven key enablers that address collaboration under various aspects such as readiness (taking into account the motivations of key people), strategic alignment (adopting a shared general vision), governance (developing a shared governance model with the OSH community), tools (having the right tools available), resources (making a strategic allocation of the available resources), value network (developing an ecosystemic collaboration), and culture (having a common collaborative culture among OSH communities). The seven enablers were defined to serve as the basis for assessing the maturity of the C3 concept.

Finally, Thomas L. and Samuel K. believe that it is essential to better understand the origin of Open-Source Hardware Business Models (OSHBMs) and to develop them because they could address their long-term viability in the context of transitioning to a Circular Economy. In this article, the city is used as the unit of analysis and the authors conducted a qualitative explorative case study of the city of Barcelona, where there are many commons-based cooperative platforms. Bringing together research on OSHBMs and the middleground construct, the authors underline the motivations and the socio-political action necessary to scale OSHBM from DIY projects to a DIT dynamic. The findings on the values and risks that are important to stakeholders are presented in a framework describing four synergy-catalyzing stages. The steps of the process the authors infer are: 1) build a tech for citizens project, 2) apply to governmental calls, 3) join the middleground and 4) build a consortium. Thus, this work provides a guide showing how OSH initiatives can leverage growth with external stakeholders.

These seven original contributions are complemented by three book reviews (see the Trends and comments section) proposed by Jean-François Boujut (INP Grenoble, G-Scope) 6 , Benjamin POUSSARD (ENSAM) 7 , and Mauricio Camargo (Université de Lorraine, ERPI) 8 . Through their critical readings, they reinforce the proposals and reflections of the twenty authors who cross paths here. Thus, they return in turn to three of the major themes that feed the Do-It-Together concept: the new values generated by Open-Source Hardware, immersive technologies as a driver in the upstream phase of innovation, and finally, beyond the technological factors, it is essential to understand, integrate and make work together the political and social and behavioral dimensions. A strong collaboration based on a joint resilient public-private system seems necessary to support this emerging model.

6

  PEARCE. J.M. (2020), Create, share and save money using open-source projects, McGraw-Hill Education, 176 p. by Jean-François Boujut (INP Grenoble, G-Scope) 7 FLEURY, S. AND RICHIR S. (2022), Immersive Technologies to Accelerate Innovation: How Virtual and Augmented Reality Enables the Co-Creation of Concepts, Smart Innovation, London, ISTE, 192 p. by Benjamin POUSSARD (ENSAM) 8 MAZZUCATO M. (2021), Mission Economy: A Moonshot Guide to Changing Capitalism, Allen Lane, 272 p. by Mauricio Camargo (Univ Lorraine, ERPI)

Acknowledgments: This issue was supported by the INEDIT European Union's H2020 research program -Agreement N° 869952.

2 https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/dt-fof-05-2019

https://www.effra.eu/factories-future, last accessed on Dec. 9th, 2022

https://www.inedit-project.eu

https://opennext.eu


Delannoy Matthieu
email: matthieu.delannoy@ensaia.inpl-nancy.fr

Duval Thibault

Fournier Agnès

Rychen Guido

Feidt Cyril

Liver, subcutaneous fat and muscle exhibit significant linear responses to increasing doses of I-PCBs in a range of 190 ng to 1310 ng of Aroclor 1254 I-PCB per Kg of Body Weight . Cerebral tissues show no evidence of linear or quadratic dose-response relationships, however this result needs to be confirmed by further experiments. These results have to be considered as an important step for further relative bioavailability studies involving complex matrices.

INTRODUCTION MATERIAL & METHODS

CONCLUSION

Cerebral tissue shows no evidence of linear or quadratic relation. This suggest that transfer of I-PCBs to the brain is not linear, however further research has to be carry out to clarify the transfer to brain.

Weak concentrations of PCB28 in Aroclor 1254 could explain very low levels in tissues (ASTDR 2000)

Liver samples exhibit higher slope than other organs suggesting a heavy bound of this contaminant to hepatic macromolecules like enzymes.

The different I-PCBs congeners were found to show similar responses Linear model significantly fit data for liver, muscle and adipose samples. In general liver tissues exhibit higher responses than other target tissues In cerebral tissue I-PCBs are not really linear Dose-response relationship appeared to be linear in all studied tissues except the cerebral one.

N.S. : p<0,05 Once a day :

In order to assess linearity regression, a statistical analysis was performed to evaluate regression coefficients and correlation of the model to data.

  hand-to-mouth activity, children (1-3 yrs) are particularly exposed to PCBs through soil ingestion. Since piglets are considered as valuable model for children, this animal specie has been used to establish linearity of dose-responses I-PCBs (USEPA 2006) . The aim of the study was to assess both linearity and levels of I-PCBs in 4 tissues: liver, Levels of responses (ng I-PCB / g fat) to increasing doses in the four target organs

  Statistical analysis was performed using R. A second-order polynomial model was evaluate in first. Linear regression was assessed through the R² and p-value Dose response curves were obtained by linear regression. Three I-PCBs were chosen for their representativeness of I-PCBs to illustrate doseresponse relationships.Regression analysis for PCB 52,101, 118,138 and 153 demonstrated significant linear relationships between the administrated doses and the levels measured in adipose tissue (R²>0.77), liver (R²>0.77) and muscle (R²>0.71).

PCB 28 PCB 52 PCB 101 PCB 118 PCB 138 PCB 153 PCB 180

		slope (y/x .10²)	-54,4	57,0	43,8	28,1	51,0	60,3	71,0
	Liver	R²	0,17	0,96	0,92	0,95	0,78	0,80	0,86
		p	N.S	0,001	0,001	0,000	0,001	0,009	N.S.
	Subcutaneous fat	slope(y/x .10²) R² p	13,0 0,12 N.S.	29,7 0,83 0,002	1,9 0,87 0,001	27,6 0,78 0,004	24,0 0,83 0,001	34,9 0,91 0,000	-10,2 0,77 0,004
		slope(y/x .10²)	90,4	30,5	32,2	28,1	39,1	51,1	28,1
	Muscle	R²	0,52	0,76	0,81	0,78	0,71	0,92	0,43
		p	N.S.	0,010	0,006	0,009	0,005	0,049	N.S.
	Cerebral Tissue	slope(y/x .10²) R² p	19,56 0,04 N.S.	1,18 0,43 N.S.	16,6 0,56 0,032	0,72 0,02 N.S.	1,26 0,71 0,009	51,1 0,43 N.S.	28,1 0,00 N.S.


EDITORIAL

Bias in machine learning for computer-assisted surgery and medical image processing

Members of the academic community would undoubtedly say that machine learning has changed the face of research in computer-assisted surgery and medical image processing and many laud this change. For some, the rise of new high-performance techniques implies that untold barriers of accuracy and efficiency are about to be demolished. For others, a degree of skepticism is shown toward techniques that claim results that, until recently, would have been considered patently ridiculous, resulting in a new light being shed on traditional aspects of algorithm verification (such as evaluation metrics [START_REF] Mason | Comparison of objective image quality metrics to expert radiologists' scoring of diagnostic quality of MR images[END_REF][START_REF] Reinke | Common limitations of image processing metrics: A picture story[END_REF] and experimental design [START_REF] Maier-Hein | BIAS: transparent reporting of biomedical image analysis challenges[END_REF] that influence the reported performance of these techniques.

Throughout all of this, bias in machine learning has become something of a watchword, a response to a myriad of problems currently seen in the literature regarding how machine learning is used in computer-assisted surgery and medical image processing. (Note that here we are discussing biases in the evaluation of a model rather than biases in the model's predictions themselves, although they are often related.) Many in the skeptical camp latch upon these four letters as foundational to their critiques. Upon further investigation, however, the concept of bias itself is not as a monolithic whole, but as a collection of inter-related considerations. For the moment, we will split these considerations into two types: human-centric elements of bias and methodologycentric elements of bias.

The human-centric elements of bias have permeated more widely into the general population's perception of machine learning. Racial, gender, age, and socio-economic biases, in particular, are indeed still issues. These biases arise from an under-, over-, or mis-representation of a particular group, possibly in the data itself, possibly in the applications chosen by researchers, and possibly in the composition of the researcher community. Building awareness and addressing these biases is highly important, and there are many voices more-qualified than mine dedicated to doing exactly that.

However, there are less-discussed methodology-centric elements (given in Table 1) also present in the literature. These biases concern largely whether or not the results of a paper are representative of their actual clinical context and arise from the methodology of the experiment itself in ways that may be stated or unstated. In addition, some of these biases overlap, such as a particular lack of representation (e.g. if images of a particular type of patient are annotated in a particular way, such was the case for several COVID19 detection models [START_REF] Degrave | AI for radiographic COVID-19 detection selects shortcuts over signal[END_REF] leading to feature leakage (e.g. finding this annotation determines the type of patient). Some of these biases appear to be a fundamental component of science, at least in a Lakatosian sense [START_REF] Lakatos | The methodology of scientific research programmes[END_REF]; science either relies on them to advance (i.e. incrementally improving the best-known models) or progressively identifies and models them (i.e. measuring how methods perform on different data and understanding those differences).

To take an example of the former, a degree of model selection bias is necessary to advance science in our field or at least is unavoidable due to the nature of science as a human endeavor. Every time one looks at the literature in order to narrow what models or hyper-parameters to use, one is fundamentally introducing a bias. Similarly, with the lack of papers displaying negative results or sub-par performance, the literature as a whole presents a much rosier and more optimistic situation for research prototypes than the clinic shows for well-validated systems. Metric/ranking selection bias is a large but necessary problem as metrics are necessary for interpretation but can be highly sensitive and opaque [START_REF] Maier-Hein | Why rankings of biomedical image analysis competitions should be interpreted with care[END_REF].

For the latter, consider distribution biases. With the exception of already identified elements, unrepresentativeness biases are by definition epistemic: one cannot know if the data that they have collected will be representative of clinics in general with respect to factors that have yet to be described. Although elements of this can be theoretically minimized by collecting more data, this is a relatively passive bias reduction strategy, as opposed to the active strategy of identifying and controlling these factors, but neither is completely certain to eliminate bias altogether. Some distribution biases related to data selection, even when they are identified, are epistemologically impossible to reduce, for example, the impossibility of measuring the results of different, mutually exclusive surgeries on the exact same patient. Others, such as temporal/causal shifts in the distribution even imply that any individual result would be different at the current time than it was at the time of the study, implying that some studies should come with an expiration date. A possible source for these biases is the way the annotations themselves are generated as the datasets generally only contain data where it is feasible to have annotations. In addition, this level of feasibility and accuracy change over time with different annotation and visualization tools [START_REF] Duncan | Using virtual reality to improve performance and user experience in manual correction of MRI segmentation errors by non-experts[END_REF] as well as being dependant on the experience of the annotators themselves [START_REF] Kohlberger | Evaluating segmentation error without ground truth[END_REF]. Additionally, annotations can introduce other biases due to systematic biases in the annotations themselves, such as prioritizing smoothness only in the inplane directions due to disagreement between a single user's segmentation across consecutive slices. Although this bias can be heuristically measured when there are multiple annotators, this is generally only considered to put an upper bound on the performance, rather than be interpreted as a negative bias [START_REF] Maier-Hein | BIAS: transparent reporting of biomedical image analysis challenges[END_REF].

In contrast to these more high-level and loosely defined biases, data leakage represents a collection of biases that are more grounded, less epistemic, and thus easier for us as a community to address. In fact, it is possible, and often still practical, to eliminate some in their entirety as they are simply showing the wrong information at the wrong time. For example, to avoid temporal leakage and correlation leakage, one must only ensure that the data provided is clearly separated into what can be collected prior to the actual diagnosis or image-processing step and what cannot, and for this separation to be validated by a clinical collaborator or in the clinic itself. (For a more detailed look at the various kinds of feature leakage in a more general context, see ( [START_REF] Larsen | Automated machine learning for business[END_REF], Chapter 24). Eliminating feature leakage in its entirely depends heavily on a functioning researcher-clinician relationship and a strong understanding of the clinical workflow and problem domain, something we should come to expect of our research community and, to our credit, is often the case.

However, train/test leakage is more insidious and some studies have shown it to have a large effect on the medical imaging and computer-assisted surgery literature, thus going unnoticed in the review process [START_REF] Samala | Hazards of data leakage in machine learning: a study on classification of breast cancer using deep neural networks[END_REF][START_REF] Yagis | Deep learning in brain MRI: Effect of data leakage due to slice-level split using 2D convolutional neural networks[END_REF] both investigate this in two different applications, for example). Simply put, train/test leakage is when information from the test set is provided in training time, although the simplicity of this formula denies the complexity of the problem as different data points themselves are correlated and provide information about each other. To give an example, several medical image computing datasets include multiple images of the same patient across different time points or even just twodimensional slices from the same three-dimensional volume. (One reason why these types of errors are so insidious is the complexity of some evaluation methods, such as cross-validation and nested cross-validation, where the difference between a correct and a leaking implementation can be difficult to detect for both authors and reviewers.) There are obvious correlations here that could be leaked if images from the same patient found themselves in both the training and the evaluation datasets at the same time. However, this is also the case with particular hospital centers. Should multi-center experiments require all of the images from each center to fall on the same side of the training/evaluation divide?

In our field of study, there is no simple answer to these questions as bias is not a matter of type but of degree. Similarly, for algorithms adjacent to the primary machine learning task, (such as data normalization or detecting invalid input,) some small amounts of leakage might have negligible detrimental effects, but also non-negligible positive effects. For example, examining the entire dataset to see what constitutes an artifact or a missing value (e.g. is a default value used? a NaN?) is technically leakage as you would also be examining the evaluation dataset. However, the positive gain from this (i.e. the removal of elements that would otherwise break the algorithm or the design of non-intelligent methods to detect these invalid inputs in the clinic) would be immense, although this is a manner Table 1. Some examples of methodology-centric elements of bias and their definitions.

Distribution biases

Unrepresentativeness: when the distribution of the evaluation data used in the paper differs from that observed in the clinic, due to the influence of human-centric elements of bias or other (possibly unknown) factors Data selection bias: when the dataset itself captures only a portion of the relevant distribution due to experimental design, technology, or feasibility reasons Annotation bias: when the annotations themselves include a consistent bias or source of error, such as simplification or differences between the inplane and out-of-plane quality Temporal/causal shift: when the distribution itself changes over time, possibly due to the influence of the system under investigation Methodology selection biases Model selection bias: when the model with the highest measured performance is selected from a group of measured models and said measurement performance is directly reported (i.e. no independent evaluation) Metric/ranking selection bias: differences between the metrics used to evaluate a framework and the underlying ill-defined notion of clinical quality (an example of this is how one deals with class imbalance) Data leakage Feature leakage: when certain features are provided as input to an algorithm when they would be unavailable in practice temporal leakage: when information that is normally not available until after the machine learning task is provided as input for said task correlation leakage: when a feature is irrelevantly correlated with the prediction in the experiment but not in intended use Train/test leakage: information from the evaluation (i.e. testing) dataset is used at least partially to develop the models which are then evaluated on said dataset normalization/imputation/augmentation leakage: when knowledge of the evaluation dataset is used to develop algorithms besides the primary one stacking/boosting/parameter leakage: when the evaluation set is used in the process of combining or selecting from multiple models or configurations of degree in terms of the methods being implemented and the knowledge is extracted. One reaction to this would be complete isolation, that researchers identify and remove all possible elements of bias, and this would be a valid response if these biases were a matter of type and not of degree. For example, in some contexts, it may be appropriate to ensure that all data arising from one hospital center is kept entirely separate from another. In others, the bias introduced by mixing centers in the dataset is not only negligible but irrelevant and damaging. For some research, we benefit from researchers building upon others methods using the same open datasets. In others, the improvement garnered reflects community-level overfitting more than real progress.

In my opinion, we should be unequivocal in asking authors to more carefully perform experiments and for reviewers to more carefully examine papers, cognizant of different biases. However, I don't think we should dogmatically call for their elimination. Instead, we should look for transparency and justification. Instead of eliminating all bias root-and-stem at the risk of suppressing research and meaningful contributions, we should critically question and make explicit to what degree the different biases exist and their effect. We should justify the level of bias and make them transparent for future research. We will likely find a certain level of bias to be unacceptable. (in my opinion and in the opinion of many others [START_REF] Samala | Hazards of data leakage in machine learning: a study on classification of breast cancer using deep neural networks[END_REF][START_REF] Yagis | Deep learning in brain MRI: Effect of data leakage due to slice-level split using 2D convolutional neural networks[END_REF], mixing data from a single patient across the training/testing divide requires an almost insurmountable level of justification) but this can only come about through a more open and nuanced conversation about bias. Thus, it would be a matter of informed judgment about whether or not a paper should be accepted and its results to be believed. This is a level of judgment that we should, as members of a mature research community, demand of ourselves.

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