Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma and other skin lesions, 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. Contributions of this work include the application of different debiasing techniques for artefact bias removal and the concept of instrument bias 'unlearning' for domain generalisation in melanoma detection. 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.

Convolutional Neural Networks have demonstrated humanlevel 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.

Convolutional Neural Networks have demonstrated humanlevel 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.