The paper introduces a new framework for generating medical image counterfactuals that does not rely on auxiliary generative models. By extracting causal evidence directly from a classifier, the method deterministically produces edits within user-specified regions, requiring no additional training. Experiments on real-world medical imaging datasets show that these counterfactuals alter classifier predictions while staying closer to the original image than generative baselines, offering a clearer view of the model’s decision boundary.
By David A. Kelly, Tom Yaacov, Nathan Blake, Sander Beckers, Hana Chockler
arXiv:2609.14124v1 Announce Type: cross
Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy...
By Yasin Ibrahim, Robin J. Evans, Konstantinos Kamnitsas
arXiv:2609.24879v1 Announce Type: new
Abstract: Counterfactual image generation answers questions about how a subject would have looked under retrospective, hypothetical scenarios. Recent methods hav...
By Xiaodan Xing, Rajat R. Rasal, Julia A. Meister, Sara Ghorayeb, Galvin Khara, Jessica Schrouff
The paper introduces a classifier‑free method for generating visual counterfactual explanations (VCEs) using Contrastive Analysis (CA). By separating generative factors common to two datasets from those specific to each class, the approach swaps only the salient factors to produce counterfactual images, thereby avoiding reliance on classifier decision boundaries. Leveraging StyleGAN2’s high‑quality synthesis and a feature‑space latent representation, the method supports multiple salient factors per dataset and achieves superior counterfactual quality on three medical imaging datasets.
By Yunlong He, Pietro Gori
arXiv:2605.10894v2 Announce Type: replace
Abstract: Deep learning models in medical imaging often fail when deployed in new clinical environments due to distribution shifts in demographics, scanner h...
By Moritz Stammel, Fabio De Sousa Ribeiro, Raghav Mehta, M\'elanie Roschewitz, Ben Glocker
arXiv:2607. 02596v1 Announce Type: cross Abstract: Deep learning models for medical diagnosis frequently exhibit substantial performance disparities across sensitive subgroups (e.
By Xinyu Jia, Weidong Guo, Wangyuan Zhao, Yi Guo, Zeju Li, Yuanyuan Wang