Counterfactual Diffusion Models for Mechanistic Explainability of Artificial Intelligence Models in Pathology.

ABSTRACT:

Background

Deep learning can extract predictive and prognostic biomarkers from histopathology whole slide images, but its interpretability remains elusive.

Methods

We develop and validate MoPaDi (Morphing histoPathology Diffusion), which generates counterfactual mechanistic explanations. MoPaDi uses diffusion autoencoders to manipulate pathology image patches and flip their biomarker status by changing the morphology. Importantly, MoPaDi includes multiple instance learning for weakly supervised problems. We validate our method on four datasets classifying tissue types, cancer types within different organs, center of slide origin, and a biomarker - microsatellite instability. Counterfactual transitions were evaluated through pathologists' user studies and quantitative cell an

SUBMITTER: Zigutyte L 

PROVIDER: S-EPMC11565818 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature