Conference item
Self-supervised representation learning for ultrasound video
- Abstract:
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Recent advances in deep learning have achieved promising performance for medical image analysis, while in most cases ground-truth annotations from human experts are necessary to train the deep model. In practice, such annotations are expensive to collect and can be scarce for medical imaging applications. Therefore, there is significant interest in learning representations from unlabelled raw data. In this paper, we propose a self-supervised learning approach to learn meaningful and transfera...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Accepted manuscript, 1.4MB)
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- Publisher copy:
- 10.1109/ISBI45749.2020.9098666
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Authors
Funding
Bibliographic Details
- Publisher:
- IEEE Publisher's website
- Publication date:
- 2020-05-22
- Acceptance date:
- 2020-01-06
- Event title:
- IEEE International Symposium on Biomedical Imaging (ISBI'20)
- Event location:
- Iowa City, IA, USA
- Event website:
- http://2020.biomedicalimaging.org/
- Event start date:
- 2020-04-03T00:00:00Z
- Event end date:
- 2020-04-07T00:00:00Z
- DOI:
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1090022
- Local pid:
- pubs:1090022
- Deposit date:
- 2020-02-28
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2020
- Rights statement:
- © 2020 IEEE.
- Notes:
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This is the accepted manuscript version of the article. The final version is available from IEE at https://doi.org/10.1109/ISBI45749.2020.9098666
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