Figure 4 from Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology

Résumé fourni par la source

Independent classifier validation and representative bidirectional counterfactual transformations in a multiclass setting. A, External classifier responses to counterfactual morphing. Counterfactual images were generated at increasing morphing amplitudes (α) with MoPaDi and then encoded with three foundation models (UNI2, CONCH, and Virchow2). Independent classifiers were trained on the corresponding encoders’ features extracted from all TCGA-CRC tiles and then used to predict the target probability P(target) on both the original and counterfactual tiles. The resulting change ΔP(target) reflects how strongly the morphing affected class evidence. Bars show the median ΔP(target) with percentile-based variability across tiles. B, Representative examples of bidirectional counterfactual explanations for MSIL patients. We defined MSIL by fitting a two-component Gaussian mixture model to the log-transformed distribution of total MSI events and using the intersection of the two components as the cutoff separating MSIL from MSIH samples.

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