While deep learning based approaches have demonstrated expert-level performance in dermatological diagnosis tasks, they have also been shown to exhibit biases toward certain demographic attributes, particularly skin types (e.g., light versus dark), a fairness concern that must be addressed. We propose CIRCLe, a skin color invariant deep representation learning method for improving fairness in skin lesion classification. CIRCLe is trained to classify images by utilizing a regularization loss that encourages images with the same diagnosis but different skin types to have similar latent representations. Through extensive evaluation and ablation studies, we demonstrate CIRCLe's superior performance over the state-of-the-art when evaluated on 16k+ images spanning 6 Fitzpatrick skin types and 114 diseases, using classification accuracy, equal opportunity difference (for light versus dark groups), and normalized accuracy range, a new measure we propose to assess fairness on multiple skin type groups.

本论文提出了一种基于CIRCLe的深度表示学习算法，通过添加正则化损失函数实现在皮肤类型不同的图像表现出相似的潜在表示，从而提高皮损分类的公平性，并在16k+张图像上进行了评估和消融研究，证明了其在使用分类准确性，对浅色与深色人群的平等机会差异和标准化准确性范围（一种新的公平性评估方法）上优于现有技术水平的表现。

CIRCLe：用于皮肤病变无偏分类的颜色不变表示学习