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. We also demonstrate the generalisation benefits of unlearning spurious variation relating to the imaging instrument used to capture lesion images. We show that the biases introduced by surgical markings and rulers presented in previous studies can be reasonably mitigated using these bias removal methods.
In this work, we robustly remove bias and spurious variation from an automated melanoma classification pipeline using two leading bias unlearning techniques. Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma ClassificationPeter Bevan, Amir Atapour-AbarghoueiConvolutional Neura. %X 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. This colour gradient resulted from the action. %C Proceedings of Machine Learning Research Human skin comes in a range colours, grading from very dark brown near the equator to near ivory near the poles. %B Proceedings of the 39th International Conference on Machine Learning %T Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification The song includes over 50 musicians from coast to coast featuring Buddy Guy, Tom Morello, Billy Branch, Chicago Childrens Choir, and Roots Gospel Voices of.
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