Explainable AI and its instantiation in Computational Pathology for a better understanding of Alzheimer’s disease

Abstract

AbstractBackgroundEXplainable Artificial Intelligence (XAI) has an essential role to accelerate the AI adoption by empowering biomedical experts and catalyzing modern research through the emergence of a new generation of virtual actors, enabling traceable knowledge‐based quantification, as a guided qualification support. XAI instantiation is shown through our study, hypothesizing that rapid Alzheimer’s Disease (rAD) brains display subtle histological changes that would be undercovered by high‐throughput automated microscopic analysis. We study the topography and morphology of tau lesions’ aggregates to better understand the morphological substratum of Alzheimer’s Disease (AD) heterogeneity.MethodTo address this question at a large scale, we designed, tested and implemented a software for automatic segmentation, annotation and quantitation of brain lesions in histopathological whole slide images (WSI). A dataset of 15 whole slide images of postmortem human brain tissue is used in this study. These are fully annotated by neuropathologists yielding a set of more than 30,000 annotated plaques and tangle objects. A deep learning pipeline consisting of an attention‐UNet model with explainability features is trained for segmentation of tau aggregates in WSIs. The attention maps corresponding to segmentation predictions from the model are used by pathologists as a comparable reference to assist their decision making process.ResultThe DL‐based WSI analysis framework allows detecting a majority of the tau aggregates and refining tau aggregate boundaries with higher accuracy compared to manual annotations. Besides, with such a DL model based tau identification, the pathologist needs to annotate only a few samples for the DL model training. Overall, DL assisted analysis of tau aggregates could ease the effort of pathologists and facilitate analysis of hundreds of WSIs which are vital in AD research areas such as identifying different AD forms and AD patient stratification.ConclusionXAI is not only providing traceability by opening the way to an effective adoption, but also allows optimizing the ML/DL design, by keeping high performances. The green footprint is, therefore, considered, as well as the GDPR (European General Data Protection Regulation), towards Responsible Artificial Intelligence.