2022-06-28-bevan22a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification	Proceedings of the 39th International Conference on Machine Learning	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	bevan22a	0	Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification	1874	1892	1874-1892	1874	false	Bevan, Peter and Atapour-Abarghouei, Amir	given family Peter Bevan given family Amir Atapour-Abarghouei	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/bevan22a/bevan22a.pdf