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.

翻译：卷积神经网络已从皮肤病变图像中实现了在黑色素瘤分类方面与皮肤科医生相当的性能，但由于训练数据中存在的偏差所导致的预测不规律性，是在广泛部署前必须解决的问题。在本研究中，我们利用两种主流的偏差遗忘技术，稳健地消除了自动化黑色素瘤分类流程中的偏差与虚假变异。我们证明，既往研究中由手术标记和标尺引入的偏差可通过这些去偏方法得到合理缓解。同时，我们还展示了遗忘与成像仪器相关的虚假变异所带来的泛化优势。实验结果表明，每种上述偏差的影响均被显著降低，且不同的去偏技术在应对不同任务时各具优势。