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2019 | OriginalPaper | Chapter

Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning

Authors : Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre, Zach Eaton-Rosen, Lewis J. Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M. Jorge Cardoso

Published in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Publisher: Springer International Publishing

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Abstract

Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to n target domains (as long as there is paired data covering all domains). Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning. We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains. The proposed method significantly outperforms other domain adaptation baselines.
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Metadata
Title
Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning
Authors
Mauricio Orbes-Arteaga
Thomas Varsavsky
Carole H. Sudre
Zach Eaton-Rosen
Lewis J. Haddow
Lauge Sørensen
Mads Nielsen
Akshay Pai
Sébastien Ourselin
Marc Modat
Parashkev Nachev
M. Jorge Cardoso
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_7

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