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Self-supervised representation learning for ultrasound video

Abstract:

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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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Women's & Reproductive Health
Role:
Author
ORCID:
0000-0002-5588-1410
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Women's & Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-3060-3772
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:
Pubs id:
1090022
Local pid:
pubs:1090022
Language:
English
Keywords:

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