Seeing What Shouldn't Be There: Counterfactual GANs for Medical Image Attribution

ArXi:2605.05283v1 Announce Type: new Ascription of an image gives insights into the objects that influence the classification of the whole image or its pixels towards a specific category. These insights help radiologists to visualize deformities in medical imaging. Most of the existing visualization techniques are based on discriminative models and highlight regions of the input image participating in the decision-making of a classifier.