arXiv AI By Yasin Ibrahim, Robin J. Evans, Konstantinos Kamnitsas

To do($x$) or not to do($x$): Medical Image Counterfactuals for Dataset Augmentation

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

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
arXiv Machine Learning
5d ago

A Causal Bootstrap for Diverse Image Generation under Unobserved Confounding

The paper introduces CauVaDE, a causal generative model that represents an unobserved confounder as a discrete latent cluster, enabling accurate approximation of both observational and interventional image distributions. It demonstrates that this representation can capture diverse causal explanations without sacrificing fit to the data. A novel causal bootstrap method is proposed to vary confounder entropy, producing a family of distinct interventional image distributions.

By Jingyuan Chen, Kangrui Ruan, Junzhe Zhang
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.