On the Fairness of Privacy-Preserving Representations in Medical Applications
Sarhan MH, Navab N, Eslami A, Albarqouni S (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12444 LNCS
Pages Range: 140-149
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lima, PER
ISBN: 9783030605476
DOI: 10.1007/978-3-030-60548-3_14
Abstract
Representation learning is an important part of any machine learning model. Learning privacy-preserving discriminative representations that are invariant against nuisance factors is an open question. This is done by removing sensitive information from the learned representation. Such privacy-preserving representations are believed to be beneficial to some medical and federated learning applications. In this paper, a framework for learning invariant fair representations by decomposing the learned representation into target and sensitive codes is proposed. An entropy maximization constraint is imposed on the target code to be invariant to sensitive information. The proposed model is evaluated on three applications derived from two medical datasets for autism detection and healthcare insurance. We compare with two methods and achieve state of the art performance in sensitive information leakage trade-off. A discussion regarding the difficulties of applying fair representation learning to medical data and when it is desirable is presented.
Involved external institutions
Germany (DE)How to cite
APA:
Sarhan, M.H., Navab, N., Eslami, A., & Albarqouni, S. (2020). On the Fairness of Privacy-Preserving Representations in Medical Applications. In Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 140-149). Lima, PER: Springer Science and Business Media Deutschland GmbH.
MLA:
Sarhan, Mhd Hasan, et al. "On the Fairness of Privacy-Preserving Representations in Medical Applications." Proceedings of the 2nd MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the 1st MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020, Lima, PER Ed. Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu, Springer Science and Business Media Deutschland GmbH, 2020. 140-149.
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