公平胸部X光诊断中的表示解缠
Representation Disentanglement for Fair Chest X-Ray Diagnosis
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中文总结 AI 辅助
本文提出一种结合双层级去相关与原型引导对比学习的单编码器框架，并引入DRAR指标，在CheXpert数据集上显著降低人口统计偏差，提升交叉群体公平性。
中文摘要 AI 辅助
深度学习推进了胸部X光（CXR）诊断，但学习到的表示中的人口统计偏差可能导致跨交叉群体的性能差异。我们提出了一个单编码器框架，结合双层级去相关与原型引导的跨组对比学习，以减少人口统计依赖性，同时考虑类内变异。我们进一步提出了人口统计表示对齐减少（DRAR）指标，该指标量化了疾病表示中人口统计结构的减少程度。该框架在四个分类任务上进行了评估，使用了34,809张CheXpert测试图像，涵盖由年龄、性别和种族定义的八个交叉群体。与经验风险最小化（ERM）相比，我们的方法将平均均等化几率差距从15.41%降至10.86%，AUC差距从5.95%降至5.01%。我们的方法相对于ERM实现了59.04%的DRAR，仅平均AUC略有下降。这些结果表明，表示解缠可以减少人口统计偏差并改善交叉公平性。代码可在 \u200b\u200b此 https URL 获取。
英文摘要
Deep learning has advanced chest X-ray (CXR) diagnosis, yet demographic biases in learned representations may contribute to performance disparities across intersectional groups. We propose a single-encoder framework combining dual-level decorrelation with prototype-guided cross-group contrastive learning to reduce demographic dependence while accounting for within-class variation. We further propose Demographic Representation Alignment Reduction (DRAR), a new metric that quantifies the reduction in demographic structure within disease representations. The framework is evaluated on four classification tasks using 34,809 CheXpert test images across eight intersectional groups, defined by age, sex and ethnicity. Compared with empirical risk minimization (ERM), our method reduces the mean equalized-odds gap from 15.41\% to 10.86\% and the AUC gap from 5.95\% to 5.01\%. Our method achieves a DRAR of 59.04\% relative to ERM, with only a slight decrease in mean AUC. These results demonstrate that representation disentanglement can reduce demographic bias and improve intersectional fairness. Code is available at \url{https://github.com/06Yujie/Fair-Medical-Imaging}.
发表机构
- Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham（诺丁汉大学医学院诺丁汉生物医学研究中心）
机构由 AI 辅助整理，请以论文原文为准。
