Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident performance disparities between differing skin tones should be addressed before widespread deployment. In this work, we utilise a modified variational autoencoder to uncover skin tone bias in datasets commonly used as benchmarks. We propose an efficient yet effective algorithm for automatically labelling the skin tone of lesion images, and use this to annotate the benchmark ISIC dataset. We subsequently use two leading bias unlearning techniques to mitigate skin tone bias. Our experimental results provide evidence that our skin tone detection algorithm outperforms existing solutions and that unlearning skin tone improves generalisation and can reduce the performance disparity between melanoma detection in lighter and darker skin tones.

本研究提出了一种高效有效的算法，可以自动标记皮肤损伤图像的肤色，并将其用于注释基准ISIC数据集以减少皮肤色调偏差。实验结果证明，皮肤色调检测算法优于现有解决方案，取消偏见可以提高普适性，并减少轻重肤色间的黑色素瘤检测性能差异。

公正检测黑色素瘤：肤色检测和去偏见技术用于皮损分类