论文标题
公平地探测黑色素瘤：皮肤病变分类的肤色检测和伪造
Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification
论文作者
论文摘要
卷积神经网络在黑色素瘤和其他皮肤病变的分类中表现出人类水平的表现，但是在广泛部署之前，应解决不同肤色之间的明显性能差异。在这项工作中，我们提出了一种有效但有效的算法，用于自动标记病变图像的肤色，并使用它来注释基准ISIC数据集。随后，我们将这些自动标签用作两种领先的偏见，无法减轻肤色偏差的目标。我们的实验结果提供了证据，表明我们的肤色检测算法优于现有的解决方案，并且牙齿肤色可以改善概括，并可以减少黑色素瘤检测到更轻和较深的肤色之间的性能差异。
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.
