Generating Medical Image Counterfactuals using Causal Explanations

2026-09-02

AI Summary

本文提出一种无需生成模型的反事实图像生成框架，直接从分类器中提取因果证据构造反事实图像，以提高医疗图像诊断模型的可解释性。

Abstract

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 using auxiliary models, including generative adversarial networks and diffusion models. While often capable of producing visually realistic images, these methods explain one black-box model using another, making it difficult to separate the classifier's decision-making process from the inductive biases of the generator. We propose a novel counterfactual-generation framework that requires no generative model. Instead, counterfactuals are constructed directly from causal evidence extracted from the classifier. The resulting approach is deterministic, requires no additional model training, and enables controllable edits within user-specified regions of interest. Experiments on real-world medical imaging datasets demonstrate that the proposed method successfully changes classifier predictions while remaining closer to the original image than generative baselines, providing a more direct and transparent view of the classifier's decision boundary.

Problem

• medical image diagnosis
• explainability
• counterfactual images
• generative models
• decision-making process

Innovation

• counterfactual generation
• causal evidence
• deterministic
• no additional model training
• controllable edits