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Incremental learning of fetal heart anatomies using interpretable saliency maps

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Conference paper
Abstract:

While medical image analysis has seen extensive use of deep neural networks, learning over multiple tasks is a challenge for connectionist networks due to tendencies of degradation in performance over old tasks while adapting to novel tasks. It is pertinent that adaptations to new data distributions over time are tractable with automated analysis methods as medical imaging data acquisition is typically not a static problem. So, one needs to ensure that a continual learning paradigm be ensured...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-39343-4_11

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Department:
ENGINEERING SCIENCE
Sub department:
Engineering Science
Role:
Author
Publisher:
Springer Publisher's website
Journal:
Communications in Computer and Information Science Journal website
Volume:
1065
Pages:
129-141
Host title:
MIUA 2019: Medical Image Understanding and Analysis
Publication date:
2020-01-24
Acceptance date:
2019-04-15
Event title:
MIUA 2019: Medical Image Understanding and Analysis
Event location:
Liverpool, UK
Event website:
https://miua2019.com/
Event start date:
2019-07-24T00:00:00Z
Event end date:
2019-07-26T00:00:00Z
DOI:
EISSN:
1865-0937
ISSN:
1865-0929
ISBN:
9783030393427
Language:
English
Keywords:
Pubs id:
1087859
Local pid:
pubs:1087859
Deposit date:
2020-05-14

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