Counterfactual Diffusion Models for Interpretable Explanations of Artificial Intelligence Models in Pathology
Laura Žigutytė, Tim Lenz, Tianyu Han, Nic Gabriel Reitsam, Sebastian Foersch, Katherine Jane Hewitt, Moritz Jesinghaus, Zunamys Itzell Carrero, Michaela Unger, Asier Rabasco Meneghetti, Georg Lurje, Isabella Lurje, Sophia Herda, Justus Pein, Deniz Uluk, , Jakob Nikolas Kather
MoPaDi generates counterfactual explanations for histopathology images to reveal morphological features driving classifier predictions, improving interpretability in digital pathology.
Cancer Research · Jun 17, 2026Peer-reviewedBioinformatics