Graph representation learning & explainability in breast cancer pathology: bridging the gap between AI and pathology practice

While cancer cases continue to increase and diagnosis, prognosis and treatment become more digital, AI-assisted cancer patient care, in particular in the pathology daily practice, remains scarce and rudimentary. In this chapter, we focus on reducing the gap between the AI technologies' outcomes and the way pathologists interpret the content of a histology image. We propose to leverage a semantic approach for both representing the relevant histology images and learning from them, thus mapping content to functionality and phenotype, that we call HistoCartography. We construct HierArchical Cell-to-Tissue (HACT) graphs to represent the content, leverage graph neural networks to learn from the HACT representations and respective graph explainers to indicate the image content that drives the AI-technologies' outputs. We further introduce a post-hoc graph explainer to quantitatively and qualitatively map the decision driving histology image content to measurable, pathologically understandable concepts. We test and validate the proposed approach by classifying seven breast carcinoma subtypes and demonstrate its power with respect to classification accuracy but moreover with respect to its ability to correlate to pathological knowledge and acceptance by domain experts, as validated by three independent pathologists from different institutions.