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Spatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scans

Abstract:

This paper considers automatic clinical workflow description of full-length routine fetal anomaly ultrasound scans using deep learning approaches for spatio-temporal video analysis. Multiple architectures consisting of 2D and 2D + t CNN, LSTM, and convolutional LSTM are investigated and compared. The contributions of short-term and long-term temporal changes are studied, and a multi-stream framework analysis is found to achieve the best top-l accuracy =0.77 and top-3 accuracy =0.94. Automated...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ISBI.2019.8759149

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Department of Women's and Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Department of Women's and Reproductive Health
Role:
Author
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Publisher:
IEEE Publisher's website
Journal:
IEEE International Symposium on Biomedical Imaging (ISBI) Journal website
Pages:
987-990
Host title:
IEEE International Symposium on Biomedical Imaging (ISBI 2019)
Publication date:
2019-07-11
Acceptance date:
2018-12-18
DOI:
EISSN:
1945-8452
ISSN:
1945-7928
Source identifiers:
965466
ISBN:
9781538636428
Pubs id:
pubs:965466
UUID:
uuid:3b8ec67e-c443-40ac-a2ef-e8da14fb5f62
Local pid:
pubs:965466
Deposit date:
2019-01-25

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