Explicabilité en Intelligence Artificielle ; vers une IA Responsable: Instanciation dans le domaine de la santé

International audienceEssential for a good adoption, as well as for a wise and unbiased use, explicability is a real technology lock to the evolution of Artificial Intelligence (AI), in particular concerning Machine and Deep Learning. Without an effective explicability of the proposed algorithms, these techniques will remain a black box for health (and not only) professionals, researchers, engineers and technicians - who assume (and will continue to assume) the full responsibility of their actions. Increasingly, engineers and designers of AI tools will have to demonstrate their responsibility by providing algorithms that guarantee the explicability of the proposed models. This article presents the motivations of an explainable AI, the main characteristics of the conceptual landscape of explainability in AI, the major families of explainability methods - with a focus on some of the most common methods, to finally present some of the opportunities, challenges and perspectives of this exciting field of human-machine interaction. Indeed, only through a good understanding of the challenges associated with this technological revolution that we will be able to transform AI into assets for our companies as well as for our human actors, partners and customers.Essentielle pour une adoption efficace comme pour une utilisation avisée et objective de l'Intelligence Artificielle (IA), l'explicabilité est un véritable verrou de l'évolution de ces technologies, en particulier concernant l'apprentissage automatique et profond. Sans une réelle explicabilité des algorithmes proposés, ces technologies resteront une boîte noire pour les professionnels de santé (et pas seulement), chercheurs, ingénieurs, techniciens - qui assument (et vont continuer à assumer) la pleine responsabilité de leurs actes.De plus en plus, les ingénieurs exploitants et concepteurs d'outils d'IA devront donc faire preuve de responsabilité, en fournissant des algorithmes permettant de garantir l'explicabilité des modèles proposés.Cet article présente les motivations d'une IA explicable, les principales caractéristiques du paysage conceptuel de l'explicabilité en IA, les grandes familles de méthodes pour l'explicabilité - avec un focus sur quelques méthodes parmi les plus courantes, pour finir sur un aperçu des opportunités, challenges et perspectives de ce domaine passionnant de l'interaction homme-machine.En effet, c'est uniquement par une bonne compréhension des challenges associés à cette révolution technologique que nous pourrons la transformer en atout pour nos entreprises ainsi que pour l'ensemble de nos acteurs, partenaires et clients humains

PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies

Abstract Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases’ characterization. https://github.com/ounissimehdi/PhagoStat

Unraveling Systematic Biases in Brain Segmentation: Insights from Synthetic Training

International audienceThis study examines how the quality of ground truth labels affects brain MRI segmentation models. We investigate the potential of synthetic learning to mitigate systematic biases present in training labels. Through a validation on high-quality datasets, in the Putamen region, known for systematic segmentation errors like the inclusion of parts of the Claustrum, we demonstrate the effectiveness of the synthetic data approach in correcting these errors and enhancing segmentation accuracy. Our findings highlight the limitations of pseudo-ground truth labels derived from automated techniques and underscores the importance of precise, expert-validated labels for accurate, unbiased validation

Unraveling Systematic Biases in Brain Segmentation: Insights from Synthetic Training

International audienceThis study examines how the quality of ground truth labels affects brain MRI segmentation models. We investigate the potential of synthetic learning to mitigate systematic biases present in training labels. Through a validation on high-quality datasets, in the Putamen region, known for systematic segmentation errors like the inclusion of parts of the Claustrum, we demonstrate the effectiveness of the synthetic data approach in correcting these errors and enhancing segmentation accuracy. Our findings highlight the limitations of pseudo-ground truth labels derived from automated techniques and underscores the importance of precise, expert-validated labels for accurate, unbiased validation

Responsible artificial intelligence : a review of current trends

International audienceWhile the advent of artificial intelligence (AI) has seen an increase in computational power and development of novel technologies to expedite current human-run processes, with this breakneck speed some concerns have risen around the technology's social, legal, and ethical implications. Responsible AI (RAI) has emerged as a new discipline designed to ensure AI technologies produce equitable outcomes and that AI is developed for the betterment of humanity. In this review poster, some of the AI-related concerns are highlighted, various aspects of RAI are defined (as there current remains no real consensus to what RAI involves), some attempts at creating quantifiable AI are illustrated, and some future directions are proposed

Responsible artificial intelligence : a review of current trends

International audienceWhile the advent of artificial intelligence (AI) has seen an increase in computational power and development of novel technologies to expedite current human-run processes, with this breakneck speed some concerns have risen around the technology's social, legal, and ethical implications. Responsible AI (RAI) has emerged as a new discipline designed to ensure AI technologies produce equitable outcomes and that AI is developed for the betterment of humanity. In this review poster, some of the AI-related concerns are highlighted, various aspects of RAI are defined (as there current remains no real consensus to what RAI involves), some attempts at creating quantifiable AI are illustrated, and some future directions are proposed

Unraveling Systematic Biases in Brain Segmentation: Insights from Synthetic Training

International audienceThis study examines how the quality of ground truth labels affects brain MRI segmentation models. We investigate the potential of synthetic learning to mitigate systematic biases present in training labels. Through a validation on high-quality datasets, in the Putamen region, known for systematic segmentation errors like the inclusion of parts of the Claustrum, we demonstrate the effectiveness of the synthetic data approach in correcting these errors and enhancing segmentation accuracy. Our findings highlight the limitations of pseudo-ground truth labels derived from automated techniques and underscores the importance of precise, expert-validated labels for accurate, unbiased validation

Agrégats discrets de protéine Tau dans la maladie d'Alzheimer : détection et segmentation des plaques neuritiques et des enchevêtrements neurofibrillaires à l'aide de l'histopathologie computationnelle

International audienceTau proteins in the gray matter are widely known to be a part of Alzheimer’s disease symptoms. They can aggregate in three different structures within the brain: neurites, tangles, and neuritic plaques. The morphology and the spatial disposition of these three aggregates are hypothesised to be correlated to the advancement of the disease. In order to establish a behavioural disease model related to the Tau proteins aggregates, it is necessary to develop algorithms to detect and segment them automatically. We present a 5-folded pipeline aiming to perform with clinically operational results. This pipeline is composed of a non-linear colour normalisation, a CNN-based image classifier, an Unet-based image segmentation stage, and a morphological analysis of the segmented objects. The tangle detection and segmentation algorithms improve state-of-the-art performances (75.8% and 91.1% F1- score, respectively), and create a reference for neuritic plaques detection and segmentation (81.3% and 78.2% F1-score, respectively). These results constitute an initial baseline in an area where no prior results exist, as far as we know. The pipeline is complete and based on a promising state-of-the-art architecture. Therefore, we consider this study a handy baseline of an impactful extension to support new advances in Alzheimer’s disease. Moreover, building a fully operational pipeline will be crucial to create a 3D histology map for a deeper understanding of clinico-pathological associations in Alzheimer’s disease and the histology-based evidence of disease stratification among different sub-types.Les protéines Tau présentes dans la matière grise sont largement connues pour faire partie des symptômes de la maladie d'Alzheimer. Elles peuvent s'agréger en trois structures différentes dans le cerveau : les neurites, les enchevêtrements et les plaques neuritiques. On suppose que la morphologie et la disposition spatiale de ces trois agrégats sont corrélées à l'évolution de la maladie. Afin d'établir un modèle de maladie comportementale lié aux agrégats de protéines Tau, il est nécessaire de développer des algorithmes pour les détecter et les segmenter automatiquement. Nous présentons un pipeline à 5 volets visant à obtenir des résultats cliniquement opérationnels. Ce pipeline est composé d'une normalisation non linéaire des couleurs, d'un classificateur d'image basé sur CNN, d'une étape de segmentation d'image basée sur Unet et d'une analyse morphologique des objets segmentés. Les algorithmes de détection et de segmentation des enchevêtrements améliorent les performances de l'état de l'art (respectivement 75,8% et 91,1% de score F1), et créent une référence pour la détection et la segmentation des plaques névritiques (respectivement 81,3% et 78,2% de score F1). Ces résultats constituent une première référence dans un domaine où aucun résultat antérieur n'existe, à notre connaissance. Le pipeline est complet et basé sur une architecture prometteuse de l'état de l'art. Par conséquent, nous considérons cette étude comme une base de référence pratique d'une extension importante pour soutenir les nouvelles avancées dans la maladie d'Alzheimer. En outre, la construction d'un pipeline pleinement opérationnel sera cruciale pour créer une carte histologique 3D permettant de mieux comprendre les associations clinico-pathologiques dans la maladie d'Alzheimer et les preuves histologiques de la stratification de la maladie entre différents sous-types

Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer's Disease using weakly annotated whole slide histopathological images

International audienceQuantifying the distribution and morphology of tau protein structures in brain tissues is key to diagnosing Alzheimer's Disease (AD) and its subtypes. Recently, deep learning (DL) models such as UNet have been successfully used for automatic segmentation of histopathological whole slide images (WSI) of biological tissues. In this study, we propose a DL-based methodology for semantic segmentation of tau lesions (i.e., neuritic plaques) in WSI of postmortem patients with AD. The state of the art in semantic segmentation of neuritic plaques in human WSI is very limited. Our study proposes a baseline able to generate a significant advantage for morphological analysis of these tauopathies for further stratification of AD patients. Essential discussions concerning biomarkers (ALZ50 versus AT8 tau antibodies), the imaging modality (different slide scanner resolutions), and the challenge of weak annotations are addressed within this seminal study. The analysis of the impact of context in plaque segmentation is important to understand the role of the micro-environment for reliable tau protein segmentation. In addition, by integrating visual interpretability, we are able to explain how the network focuses on a region of interest (ROI), giving additional insights to pathologists. Finally, the release of a new expert-annotated database and the code (https://github.com/aramis-lab/miccai2022-stratifiad.git) will be helpful for the scientific community to accelerate the development of new pipelines for human WSI processing in AD