arXiv Computer Vision
Sep 3

Generating Medical Image Counterfactuals using Causal Explanations

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
Hugging Face Trending Papers
Sep 2

Generating Medical Image Counterfactuals using Causal Explanations

The paper introduces a new method for generating medical image counterfactuals that does not rely on auxiliary generative models. By extracting causal evidence directly from the classifier, the approach deterministically edits user-specified regions to alter predictions while staying closer to the original image than generative baselines. Experiments on real-world medical imaging datasets show that this technique provides a more direct and transparent view of the classifier’s decision boundary.

arXiv AI
Jun 4

Counterfactual Explanations for Deep Two-Sample Testing

arXiv:2606. 04009v1 Announce Type: cross Abstract: Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-based tests) can be ineffective on high-dimensional structured data such as images.

By Wei-Cheng Lai, Marco Simnacher, Christoph Lippert