Figure 7 from Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology

Résumé fourni par la source

Counterfactual image examples generated for the lung and breast cancer–type classifiers. A, Representative examples of LUSC tile transitioning to its counterfactual lung adenocarcinoma (LUAD) image and vice versa. B, Morphologic feature prevalence in original and counterfactual image pairs (N = 32 transitions; 16 tiles 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. C, Counterfactual image generation effectiveness, measured as the percentage of generated images predicted as the opposite class across varying manipulation amplitudes. D, Representative examples of ILC tile transitioning to its counterfactual IDC image and vice versa. Difference maps display pixelwise differences between the original and the synthetic tile. Scale bar applies to all images within the panel unless otherwise indicated.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.