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Learning spatio-temporal aggregation for fetal heart analysis in ultrasound video

Abstract:

We investigate recent deep convolutional architectures for automatically describing multiple clinically relevant properties of the fetal heart in Ultrasound (US) videos, with the goal of learning spatio-temporal aggregation of deep representations. We examine multiple temporal encoding models that combine both spatial and temporal features tailored for US video representation. We cast our task into a multi-task learning problem within a hierarchical convolutional model that jointly predicts t...

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Publication status:
Published
Peer review status:
Reviewed (other)

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Publisher copy:
10.1007/978-3-319-67558-9_32

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Exeter College
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Engineering Science
Oxford college:
St Hilda's College
Role:
Author
ORCID:
0000-0002-3060-3772
Publisher:
Springer Publisher's website
Journal:
Lecture Notes in Computer Science Journal website
Volume:
10553
Pages:
276-284
Host title:
Lecture Notes in Computer Science
Publication date:
2017-09-09
Event title:
Third International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017
Event location:
Québec City, QC, Canada
Event start date:
2017-09-14T00:00:00Z
Event end date:
2017-09-14T00:00:00Z
DOI:
ISSN:
0302-9743
Source identifiers:
732835
ISBN:
9783319675572
Language:
English
Keywords:
Pubs id:
pubs:732835
UUID:
uuid:649b20dd-c893-4f25-a85b-0dd932ff14ab
Local pid:
pubs:732835
Deposit date:
2019-05-31

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