Skin tone as a demographic bias and inconsistent human labeling poses challenges in dermatology AI. We take another angle to investigate color contrast's impact, beyond skin tones, on malignancy detection in skin disease datasets: We hypothesize that in addition to skin tones, the color difference between the lesion area and skin also plays a role in malignancy detection performance of dermatology AI models. To study this, we first propose a robust labeling method to quantify color contrast scores of each image and validate our method by showing small labeling variations. More importantly, applying our method to \textit{the only} diverse-skin tone and pathologically-confirmed skin disease dataset DDI, yields \textbf{DDI-CoCo Dataset}, and we observe a performance gap between the high and low color difference groups. This disparity remains consistent across various state-of-the-art (SoTA) image classification models, which supports our hypothesis. Furthermore, we study the interaction between skin tone and color difference effects and suggest that color difference can be an additional reason behind model performance bias between skin tones. Our work provides a complementary angle to dermatology AI for improving skin disease detection.

通过研究肤色对皮肤疾病检测性能的影响以及肤色差异与病变区域之间的色差对恶性检测的作用，我们提出了一种量化色差分数的方法并应用于多种皮肤疾病数据集。观察到高和低色差差异组之间的性能差距，这一差异在各种图像分类模型中一致存在，从而验证了我们的假设。此外，我们还研究了肤色和色差效应之间的相互作用，并提出色差可能是肤色性能偏差的另一个原因。我们的工作为改善皮肤疾病检测提供了一个补充角度。

DDI-CoCo：一份用于理解机器辅助皮肤病检测中色彩对比效果的数据集