Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

Bevan, Peter; Atapour-Abarghouei, Amir

Abstract

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. 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.

Citation

Bevan, P., & Atapour-Abarghouei, A. (2022). Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), Proceedings of Machine Learning Research (1874-1892)

• Conference Name: The 39th International Conference on Machine Learning (ICML 2022)
• Conference Location: Baltimore, MD
• Start Date: Jul 17, 2022
• End Date: Jul 23, 2022
• Acceptance Date: May 15, 2022
• Online Publication Date: Dec 6, 2022
• Publication Date: 2022
• Deposit Date: Jun 10, 2022
• Publicly Available Date: Jun 20, 2022
• Volume: 162
• Pages: 1874-1892
• Series Title: Proceedings of Machine Learning Research (PMLR)
• Series ISSN: 2640-3498
• Book Title: Proceedings of Machine Learning Research
• Public URL: https://durham-repository.worktribe.com/output/1136127
• Publisher URL: https://proceedings.mlr.press/v162/bevan22a.html