Figure 6 from Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology
Year: 2026
Type: other
Abstract: Counterfactual image examples generated for the liver cancer type classifier [hepatocellular carcinoma (HCC) vs. cholangiocarcinoma (CCA)]. A, Representative examples of counterfactual transitions generated with two approaches (linear and MIL) and a comparison of corresponding segmentation masks. B, Counterfactual image generation effectiveness, measured as the percentage of generated images predicted as the opposite class across varying manipulation amplitudes. HCC-to-CCA required higher amplitudes to achieve similar results, which likely reflects class imbalance in the dataset. Scale bar applies to all images within the panel.
Authors: Laura Žigutytė, Tim Lenz, Tianyu Han, Nic G. Reitsam, Sebastian Foersch, Katherine J. Hewitt, Moritz Jesinghaus, Zunamys I. Carrero, Michaela Unger, Asier Rabasco Meneghetti, Georg Lurje, Isabella Lurje, Sophia Herda, Justus Pein, Deniz Uluk, Carolin V. Schneider, Alexander T. Pearson, Daniel Truhn, Jakob Nikolas Kather
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7204997592