Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis

• URL: http://arxiv.org/abs/2509.24913v2
• Date: Thu, 02 Oct 2025 17:13:19 GMT
• Title: Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis
• Authors: Tian Xia, Matthew Sinclair, Andreas Schuh, Fabio De Sousa Ribeiro, Raghav Mehta, Rajat Rasal, Esther Puyol-Antón, Samuel Gerber, Kersten Petersen, Michiel Schaap, Ben Glocker,
• Abstract summary: Counterfactual image generation is a powerful tool for augmenting training data, de-biasing datasets, and modeling disease.<n>We propose Segmentor-guided Counterfactual Fine-Tuning (Seg-CFT), which preserves the simplicity of intervening on scalar-valued, structure-specific variables.<n>We demonstrate the capability of generating realistic chest radiographs, and we show promising results for modeling coronary artery disease.
• Score: 12.537078681597498
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Counterfactual image generation is a powerful tool for augmenting training data, de-biasing datasets, and modeling disease. Current approaches rely on external classifiers or regressors to increase the effectiveness of subject-level interventions (e.g., changing the patient's age). For structure-specific interventions (e.g., changing the area of the left lung in a chest radiograph), we show that this is insufficient, and can result in undesirable global effects across the image domain. Previous work used pixel-level label maps as guidance, requiring a user to provide hypothetical segmentations which are tedious and difficult to obtain. We propose Segmentor-guided Counterfactual Fine-Tuning (Seg-CFT), which preserves the simplicity of intervening on scalar-valued, structure-specific variables while producing locally coherent and effective counterfactuals. We demonstrate the capability of generating realistic chest radiographs, and we show promising results for modeling coronary artery disease. Code: https://github.com/biomedia-mira/seg-cft.

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