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.
M. Orbes-Arteaga and T. Varsavsky—Equal contribution
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Acknowledgements
We gratefully acknowledge the support of NVIDIA Corporation with the donation of one Titan Xp. This project has received funding from the EU H2020 under the Marie Skłodowska-Curie grant agreement No 721820, Wellcome Flagship Programme (WT213038/Z/18/Z) and Wellcome EPSRC CME (WT203148/Z/16/Z). Carole H. Sudre is supported by AS-JF-17-011 Alzheimer’s Society Junior Fellowship.
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Orbes-Arteaga, M. et al. (2019). Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning. In: Wang, Q., et al. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. DART MIL3ID 2019 2019. Lecture Notes in Computer Science(), vol 11795. Springer, Cham. https://doi.org/10.1007/978-3-030-33391-1_7
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DOI: https://doi.org/10.1007/978-3-030-33391-1_7
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