Figure 3 from Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology
Year: 2026
Type: other
Abstract: Interpretation of the molecular biomarker (MSI) classifier in colorectal cancer through counterfactual image analysis. A, Representative examples of MSIH tiles transitioning to their counterfactual non-MSIH image. Pixel-wise difference images compare the original image with the most manipulated one. B, Representative examples of non-MSIH tiles transitioning to their counterfactual MSIH image. Pixel-based difference maps highlight changes in nuclei, stroma, cell density, and tissue architecture. C, Morphologic feature prevalence in original and counterfactual image pairs (N = 52 transitions; 26 tiles from 13 patients for each class). Horizontal bars show the percentage of image pairs in which at least one of three raters (three board-certified pathologists) identified each morphologic feature as present in the original (dark gray) or counterfactual (light gray) image. Right, Mean pairwise inter-rater agreement (Cohen κ) per feature, computed across all pairs and both directions combined. D, Quantitative evaluation of counterfactual image generation effectiveness. Bars show the percentage of successful manipulations (i.e., predicted class equals target class) across different manipulation amplitudes. The scale bar applies to all images within the figure.
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: W7205011398