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 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 these automated labels
as the target for two leading bias unlearning techniques towards mitigating
skin tone bias. Our experimental results provide evidence that our skin tone
detection algorithm outperforms existing solutions and that unlearning skin
tone may improve 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。