@inproceedings{48be0221d00648c88ed4ec78905c2ef5,
title = "On the Fairness of Privacy-Preserving Representations in Medical Applications",
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.",
keywords = "ABIDE dataset, Fair representations, Invariant representations, Privacy-preserving representations",
author = "Sarhan, {Mhd Hasan} and Nassir Navab and Abouzar Eslami and Shadi Albarqouni",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 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 ; Conference date: 04-10-2020 Through 08-10-2020",
year = "2020",
doi = "10.1007/978-3-030-60548-3_14",
language = "English",
isbn = "9783030605476",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "140--149",
editor = "Shadi Albarqouni and Spyridon Bakas and Konstantinos Kamnitsas and Cardoso, {M. Jorge} and Bennett Landman and Wenqi Li and Fausto Milletari and Nicola Rieke and Holger Roth and Daguang Xu and Ziyue Xu",
booktitle = "Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning - 2nd MICCAI Workshop, DART 2020, and 1st MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Proceedings",
}