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Ensuring both accuracy and fairness in AI-assisted chest X-ray (CXR) diagnostics is critical, particularly given the presence of sensitive patient information. In this study, we introduce ShortCXR, a Shortcut-Infused Chest X-Ray Dataset explicitly designed to expose and measure biases stemming from intrinsic image features, data sources, and demographic factors. We then integrate self-supervised learning (SSL) techniques into a series of experiments to evaluate how these biases affect diagnostic performance. Our findings demonstrate that state-of-the-art SSL methods boost both accuracy and fairness, effectively mitigating the impact of distortion-based, source-based, and demographic biases. By highlighting SSL’s potential to enhance diagnostic quality and equity, this work provides a strong foundation for future research on bias mitigation in medical imaging.

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ShortCXR: Benchmarking Self-supervised Learning for Shortcut Mitigation in Chest X-Ray Diagnostics

  • 会议论文

  • 发表日期：

    2026年

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  • You-Qi Chang-Liao,
  • Po-Chih Kuo

摘要

Ensuring both accuracy and fairness in AI-assisted chest X-ray (CXR) diagnostics is critical, particularly given the presence of sensitive patient information. In this study, we introduce ShortCXR, a Shortcut-Infused Chest X-Ray Dataset explicitly designed to expose and measure biases stemming from intrinsic image features, data sources, and demographic factors. We then integrate self-supervised learning (SSL) techniques into a series of experiments to evaluate how these biases affect diagnostic performance. Our findings demonstrate that state-of-the-art SSL methods boost both accuracy and fairness, effectively mitigating the impact of distortion-based, source-based, and demographic biases. By highlighting SSL’s potential to enhance diagnostic quality and equity, this work provides a strong foundation for future research on bias mitigation in medical imaging.