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Ensemble of deep convolutional neural networks with Monte Carlo dropout sampling for automated image segmentation quality control and robust deep learning using small datasets

Abstract:

Recent progress on deep learning (DL)-based medical image segmentation can enable fast extraction of clinical parameters for efficient clinical workflows. However, current DL methods can still fail and require manual visual inspection of outputs, which is time-consuming and diminishes the advantages of automation. For clinical applications, it is essential to develop DL approaches that can not only perform accurate segmentation, but also predict the segmentation quality and flag poor-quality ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Role:
Author
ORCID:
0000-0002-8911-923X
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Institution:
University of Oxford
Division:
MSD
Department:
RDM
Oxford college:
Balliol College
Role:
Author
ORCID:
0000-0002-9384-4602
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Role:
Author
ORCID:
0000-0002-0127-7517
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Role:
Author
ORCID:
0000-0003-3385-1242
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Role:
Author
ORCID:
0000-0002-0046-7634
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Contributors

Role:
Editor
Role:
Editor
Role:
Editor
Role:
Editor
Role:
Editor
Publisher:
Springer Nature Publisher's website
Volume:
12722
Pages:
280-293
Series:
Lecture Notes in Computer Science
Host title:
Proceedings of the 25th UK Conference on Medical Image Understanding and Analysis (MIUA 2021)
Publication date:
2021-07-06
Acceptance date:
2021-05-04
Event title:
25th UK Conference on Medical Image Understanding and Analysis (MIUA 2021)
Event location:
Oxford, UK
Event website:
https://miua2021.com/
Event start date:
2021-07-12T00:00:00Z
Event end date:
2021-07-14T00:00:00Z
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
ISBN:
9783030804312
Language:
English
Keywords:
Pubs id:
1189180
Local pid:
pubs:1189180
Deposit date:
2021-09-27

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