Convolutional Neural Networks have demonstrated dermatologist-level
performance in the classification of melanoma from skin lesion images, but
prediction irregularities due to biases seen within the training data are an
issue that should be addressed before widespread deployment is possible. In
this work, we robustly remove bias and spurious variation from an automated
melanoma classification pipeline using two leading bias unlearning techniques.
We show that the biases introduced by surgical markings and rulers presented in
previous studies can be reasonably mitigated using these bias removal methods.
We also demonstrate the generalisation benefits of unlearning spurious
variation relating to the imaging instrument used to capture lesion images. Our
experimental results provide evidence that the effects of each of the
aforementioned biases are notably reduced, with different debiasing techniques
excelling at different tasks.

本文研究了卷积神经网络在皮肤病变图像鉴别中的应用，针对现有训练数据中存在的偏见问题，通过两种偏置解除技术对自动黑素瘤分类流水线进行了优化，并表明这些方法可以合理减轻手术标记和前期研究中出现的误差。实验证明，针对一些偏见使用不同的去偏置技术有着明显的效果改善，同时提高了黑素瘤图像分类算法的泛化能力。