Abstract
A well known problem in medical imaging is the performance degradation that occurs when using a model learned on source data, in a new site. Supervised Domain Adaptation (SDA) strategies that focus on this challenge, assume the availability of a limited number of annotated samples from the new site. A typical SDA approach is to pre-train the model on the source site and then fine-tune on the target site. Current research has thus mainly focused on which layers should be fine-tuned. Our approach is based on transferring also the gradients history of the pre-training phase to the fine-tuning phase. We present two schemes to transfer the gradients information to improve the generalization achieved during pre-training while fine-tuning the model. We show that our methods outperform the state-of-the-art with different levels of data scarcity from the target site, on multiple datasets and tasks.
Original language | English |
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Title of host publication | Domain Adaptation and Representation Transfer - 4th MICCAI Workshop, DART 2022, Held in Conjunction with MICCAI 2022, Proceedings |
Editors | Konstantinos Kamnitsas, Lisa Koch, Mobarakol Islam, Ziyue Xu, Jorge Cardoso, Qi Dou, Nicola Rieke, Sotirios Tsaftaris |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 23-32 |
Number of pages | 10 |
ISBN (Print) | 9783031168512 |
DOIs | |
State | Published - 2022 |
Event | 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 - Singapore, Singapore Duration: 22 Sep 2022 → 22 Sep 2022 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13542 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 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 |
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Country/Territory | Singapore |
City | Singapore |
Period | 22/09/22 → 22/09/22 |
Bibliographical note
Publisher Copyright:© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Funding
This research was supported by the Ministry of Science & Technology, Israel.
Funders | Funder number |
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Ministry of science and technology, Israel |
Keywords
- Gradient transfer
- MRI segmentation
- Site adaptation
- Transfer learning
- Xray classification