Peter J. Bevan
Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification
Bevan, Peter J.; Atapour-Abarghouei, Amir
Authors
Dr Amir Atapour-Abarghouei amir.atapour-abarghouei@durham.ac.uk
Assistant Professor
Contributors
Konstantinos Kamnitsas
Editor
Lisa Koch
Editor
Mobarakol Islam
Editor
Ziyue Xu
Editor
Jorge Cardoso
Editor
Qi Doi
Editor
Nicola Rieke
Editor
Sotirios Tsaftaris
Editor
Abstract
Convolutional Neural Networks have demonstrated human-level 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.
Citation
Bevan, P. J., & Atapour-Abarghouei, A. (2022). Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification. In K. Kamnitsas, L. Koch, M. Islam, Z. Xu, J. Cardoso, Q. Doi, …S. Tsaftaris (Eds.), DART 2022: Domain Adaptation and Representation Transfer (1-11). https://doi.org/10.1007/978-3-031-16852-9_1
Conference Name | DART: MICCAI Workshop on Domain Adaptation and Representation Transfer |
---|---|
Acceptance Date | Aug 15, 2022 |
Online Publication Date | Sep 15, 2022 |
Publication Date | 2022 |
Deposit Date | Sep 20, 2022 |
Publicly Available Date | Sep 16, 2023 |
Volume | 13542 |
Pages | 1-11 |
Series Title | Lecture Notes in Computer Science |
Series ISSN | 0302-9743,1611-3349 |
Book Title | DART 2022: Domain Adaptation and Representation Transfer |
ISBN | 9783031168512 |
DOI | https://doi.org/10.1007/978-3-031-16852-9_1 |
Public URL | https://durham-repository.worktribe.com/output/1135904 |
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Copyright Statement
This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/[insert DOI]. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms
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