让医学影像模型更公平，减少种族性别偏见影响诊断。
AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-rays
- 用对抗干预方法抑制敏感属性，降低偏见干扰
- 在胸部X光数据集上提升公平性与诊断准确率
- 适合医疗AI公平性研究者和临床应用开发者
对比语言-图像预训练（CLIP）模型在多种视觉任务中表现优异，包括医学图像分类。然而，对公平性问题（如人口统计学偏见）的关注仍不足，尤其在种族和性别方面，导致诊断结果差异显著，使少数群体可靠性下降。为此，我们提出AdFair-CLIP框架，通过对抗特征干预抑制敏感属性，缓解虚假相关性，提升预测公平性。我们在胸部X光（CXR）数据集上进行了全面实验，结果表明AdFair-CLIP显著提升了公平性与诊断准确性，同时保持了零样本和少样本场景下的强泛化能力。该工作为基于CLIP的医学诊断模型公平性学习设立了新基准，尤其适用于胸部X光分析。
原文摘要 · Abstract (English)
Contrastive Language-Image Pre-training (CLIP) models have demonstrated superior performance across various visual tasks including medical image classification. However, fairness concerns, including demographic biases, have received limited attention for CLIP models. This oversight leads to critical issues, particularly those related to race and gender, resulting in disparities in diagnostic outcomes and reduced reliability for underrepresented groups. To address these challenges, we introduce AdFair-CLIP, a novel framework employing adversarial feature intervention to suppress sensitive attributes, thereby mitigating spurious correlations and improving prediction fairness. We conduct comprehensive experiments on chest X-ray (CXR) datasets, and show that AdFair-CLIP significantly enhances both fairness and diagnostic accuracy, while maintaining robust generalization in zero-shot and few-shot scenarios. These results establish new benchmarks for fairness-aware learning in CLIP-based medical diagnostic models, particularly for CXR analysis.
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