Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916984625561600 |
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| author | Xia, Tian Sinclair, Matthew Schuh, Andreas Ribeiro, Fabio De Sousa Mehta, Raghav Rasal, Rajat Puyol-Antón, Esther Gerber, Samuel Petersen, Kersten Schaap, Michiel Glocker, Ben |
| author_facet | Xia, Tian Sinclair, Matthew Schuh, Andreas Ribeiro, Fabio De Sousa Mehta, Raghav Rasal, Rajat Puyol-Antón, Esther Gerber, Samuel Petersen, Kersten Schaap, Michiel Glocker, Ben |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24913 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis Xia, Tian Sinclair, Matthew Schuh, Andreas Ribeiro, Fabio De Sousa Mehta, Raghav Rasal, Rajat Puyol-Antón, Esther Gerber, Samuel Petersen, Kersten Schaap, Michiel Glocker, Ben Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Segmentor-Guided Counterfactual Fine-Tuning for Locally Coherent and Targeted Image Synthesis |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.24913 |