Explaining the black-box smoothly-A counterfactual approach.

ABSTRACT: We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., , saliency maps) that assess feature importance do not explain how imaging features in important anatomical regions are relevant to the classification decision. Such reasoning is crucial for transparent decision-making in healthcare applications. Our framework explains the decision for a target class by gradually exaggerating the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation. Our proposed loss function preserves essential details (e.g., support

SUBMITTER: Singla S 

PROVIDER: S-EPMC9835100 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature