AI可从皮肤病理图像中预测患者种族，提示存在隐性偏见风险。
Predict Patient Self-reported Race from Skin Histological Images
- 用注意力机制分析病理图像，找出与种族相关的形态特征。
- 白人与黑人群体预测准确率分别达AUC 0.799和0.762。
- 表皮区域是关键预测特征，移除后性能显著下降。
人工智能在计算病理学中已成功应用于疾病检测、生物标志物分类和预后预测，但其学习非预期人口统计学偏见（特别是与健康社会决定因素相关者）的潜力仍研究不足。本研究探究深度学习模型是否能从数字化皮肤病理切片中预测自我报告种族，并识别潜在的形态学捷径。利用多中心、种族多样化的数据集，采用基于注意力的机制挖掘与种族相关的形态特征。通过评估三种数据清洗策略以控制混杂因素，最终实验显示白人与黑人群体的预测性能仍较高（AUC：0.799，0.762），而整体性能降至0.663。注意力分析揭示表皮是关键预测特征，去除该区域后性能明显下降。研究强调需谨慎进行数据清洗与偏见缓解，以确保病理学中AI应用的公平性。代码详见：https://github.com/sinai-computational-pathology/CPath_SAIF。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) has demonstrated success in computational pathology (CPath) for disease detection, biomarker classification, and prognosis prediction. However, its potential to learn unintended demographic biases, particularly those related to social determinants of health, remains understudied. This study investigates whether deep learning models can predict self-reported race from digitized dermatopathology slides and identifies potential morphological shortcuts. Using a multisite dataset with a racially diverse population, we apply an attention-based mechanism to uncover race-associated morphological features. After evaluating three dataset curation strategies to control for confounding factors, the final experiment showed that White and Black demographic groups retained high prediction performance (AUC: 0.799, 0.762), while overall performance dropped to 0.663. Attention analysis revealed the epidermis as a key predictive feature, with significant performance declines when these regions were removed. These findings highlight the need for careful data curation and bias mitigation to ensure equitable AI deployment in pathology. Code available at: https://github.com/sinai-computational-pathology/CPath_SAIF.
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