  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.
