TY - GEN
T1 - Unsupervised Site Adaptation by Intra-site Variability Alignment
AU - Goodman, Shaya
AU - Kasten Serlin, Shira
AU - Greenspan, Hayit
AU - Goldberger, Jacob
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - A medical imaging network that was trained on a particular source domain usually suffers significant performance degradation when transferred to a different target domain. This is known as the domain-shift problem. In this study, we propose a general method for transfer knowledge from a source site with labeled data to a target site where only unlabeled data is available. We leverage the variability that is often present within each site, the intra-site variability, and propose an unsupervised site adaptation method that jointly aligns the intra-site data variability in the source and target sites while training the network on the labeled source site data. We applied our method to several medical MRI image segmentation tasks and show that it consistently outperforms state-of-the-art methods.
AB - A medical imaging network that was trained on a particular source domain usually suffers significant performance degradation when transferred to a different target domain. This is known as the domain-shift problem. In this study, we propose a general method for transfer knowledge from a source site with labeled data to a target site where only unlabeled data is available. We leverage the variability that is often present within each site, the intra-site variability, and propose an unsupervised site adaptation method that jointly aligns the intra-site data variability in the source and target sites while training the network on the labeled source site data. We applied our method to several medical MRI image segmentation tasks and show that it consistently outperforms state-of-the-art methods.
KW - Intra-site variability
KW - MRI segmentation
KW - UDA
KW - Unsupervised domain adaptation
UR - http://www.scopus.com/inward/record.url?scp=85140472370&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16852-9_6
DO - 10.1007/978-3-031-16852-9_6
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AN - SCOPUS:85140472370
SN - 9783031168512
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 56
EP - 65
BT - Domain Adaptation and Representation Transfer - 4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Kamnitsas, Konstantinos
A2 - Koch, Lisa
A2 - Islam, Mobarakol
A2 - Xu, Ziyue
A2 - Cardoso, Jorge
A2 - Dou, Qi
A2 - Rieke, Nicola
A2 - Tsaftaris, Sotirios
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 22 September 2022 through 22 September 2022
ER -