Figure: Overview of the GCA Framework
Figure: Overview of the GCA Framework
Deep learning (DL) models trained for chest x-ray (CXR) classification can encode protected demographic attributes and exhibit bias towards underrepresented patient populations. In this work, we propose Generative Counterfactual Augmentation (GCA), a framework for mitigating algorithmic bias through demographic-complete augmentation of training data. We use a StyleGAN3-based synthesis network and SVM-guided latent space traversal to generate structured age and sex counterfactuals for each CXR while preserving disease features. We extensively evaluate GCA for training DL models with the RSNA Pneumonia dataset using controlled underdiagnosis bias injection across age- and sex-groups at varying rates. Our results show up to 23% reduction in FNR disparity, with a mean reduction of 9%, across varying rates of underdiagnosis bias. When evaluated with the external CheXpert and MIMIC-CXR datasets, we observe a consistent FNR reduction and improved model generalizability. We demonstrate that GCA is an effective strategy for mitigating algorithmic bias in DL models for medical imaging, ensuring trustworthiness in clinical settings.
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GCA offers a scalable and effective framework for mitigating algorithmic bias in DL models through structured counterfactual generation. Our findings suggest that GCA not only improves model fairness and robustness but also has the potential to be adapted for other imaging modalities and tasks, ensuring trustworthiness in clinical settings.
@inproceedings{uwaeze2025generative,
title={Generative Counterfactual Augmentation for Bias Mitigation},
author={Uwaeze, Jason and Kulkarni, Pranav and Braverman, Vladimir and Jacobs, Michael A and Parekh, Vishwa S},
booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
pages={[pages]},
year={2025},
}
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