用色度计验证的神经网络，自动估算皮肤类型，助力皮肤镜图像模型公平性审计
Colorimeter-Supervised Skin Tone Estimation from Dermatoscopic Images for Fairness Auditing
- 通过序数回归与颜色回归，从皮肤镜图像预测菲茨帕特里克皮肤类型和个体体型角
- 模型预测结果与色度计测量值高度一致，显著优于传统像素平均法
- 适用于皮肤镜模型公平性评估，尤其适合关注肤色差异的研究者
基于神经网络的皮肤镜图像诊断在临床决策支持中日益普及，但研究发现不同肤色间存在性能差异。当前对这些模型的公平性审计受限于公开皮肤镜数据集中缺乏可靠的肤色标注。本文提出神经网络模型，通过序数回归预测菲茨帕特里克皮肤类型，通过颜色回归预测个体体型角（ITA），以现场采集的菲茨帕特里克标签和色度计测量值为监督信号。模型在合成与真实皮肤镜及临床图像上进行大规模预训练。结果显示，菲茨帕特里克预测一致性接近人工众包标注水平，ITA预测与色度计结果高度吻合，显著优于像素平均方法。应用于ISIC 2020与MILK10k数据集时，仅不到1%的受试者属于菲茨帕特里克类型V和VI。代码与预训练模型已开源，可快速实现肤色标注与偏见审计。这是首个基于色度计测量验证的皮肤镜肤色估计神经网络，支持肤色群体间存在临床相关性能差距的证据。
原文摘要 · Abstract (English)
Neural-network-based diagnosis from dermatoscopic images is increasingly used for clinical decision support, yet studies report performance disparities across skin tones. Fairness auditing of these models is limited by the lack of reliable skin-tone annotations in public dermatoscopy datasets. We address this gap with neural networks that predict Fitzpatrick skin type via ordinal regression and the Individual Typology Angle (ITA) via color regression, using in-person Fitzpatrick labels and colorimeter measurements as targets. We further leverage extensive pretraining on synthetic and real dermatoscopic and clinical images. The Fitzpatrick model achieves agreement comparable to human crowdsourced annotations, and ITA predictions show high concordance with colorimeter-derived ITA, substantially outperforming pixel-averaging approaches. Applying these estimators to ISIC 2020 and MILK10k, we find that fewer than 1% of subjects belong to Fitzpatrick types V and VI. We release code and pretrained models as an open-source tool for rapid skin-tone annotation and bias auditing. This is, to our knowledge, the first dermatoscopic skin-tone estimation neural network validated against colorimeter measurements, and it supports growing evidence of clinically relevant performance gaps across skin-tone groups.
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