Paper recorded by Signals 4 on 2026-09-02 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03
Category: cs.CV · 计算机视觉 · first seen 2026-09-03
Deep learning models have achieved impressive performance in medical image diagnosis, yet their deployment in clinical settings remains constrained by limited explainability. Counterfactual images provide one means of auditing model behavior by showing how an image would need to change for a classifier to produce a different prediction. Existing approaches typically generate such explanations usin