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.
@article{arxiv.1908.05959,
title = {Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning},
author = {Mauricio Orbes-Arteaga and Thomas Varsavsky and Carole H. Sudre and Zach Eaton-Rosen and Lewis J. Haddow and Lauge Sørensen and Mads Nielsen and Akshay Pai and Sébastien Ourselin and Marc Modat and Parashkev Nachev and M. Jorge Cardoso},
journal= {arXiv preprint arXiv:1908.05959},
year = {2019}
}
Comments
Accepted at 1st International Workshop on Domain Adaptation and Representation Transfer held at MICCAI 2019