论文标题
圆圈：颜色不变的表示皮肤病变的无偏分类
CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions
论文作者
论文摘要
虽然基于深度学习的方法在皮肤病学诊断任务中表现出了专家级的表现，但它们还显示出对某些人口统计学属性的偏见，尤其是皮肤类型（例如，光对黑暗），必须解决公平的关注。我们提出了圆圈，这是一种肤色不变的深度表示学习方法，可改善皮肤病变分类的公平性。通过利用正规化损失来鼓励具有相同诊断的图像但皮肤类型不同的潜在表示，可以通过使用正规化损失来对图像进行分类。通过广泛的评估和消融研究，我们证明了在跨越6种菲茨帕特里克皮肤类型和114种疾病的16K+图像上进行评估时，Circle的表现优于最先进的表现，使用分类精度，平等的机会差异（对于光线相对于黑暗组）以及归一化的精度范围，我们提议在多种皮肤类型组上评估一项新的措施。
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.
