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OXENDONET: A dilated convolutional neural networks for endoscopic artefact segmentation

Abstract:

Medical image segmentation plays a key role in many generic applications such as population analysis and, more accessibly, can be made into a crucial tool in diagnosis and treatment planning. Its output can vary from extracting practical clinical information such as pathologies (detection of cancer), to measuring anatomical structures (kidney volume, cartilage thickness, bone angles). Many prior approaches to this problem are based on one of two main architectures: a fully convolutional netwo...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publication website:
http://ceur-ws.org/Vol-2595/

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
Publisher:
CEUR Workshop Proceedings
Journal:
CEUR Workshop Proceedings More from this journal
Volume:
2595
Pages:
26-29
Publication date:
2020-01-01
Event title:
2nd International Workshop and Challenge on Computer Vision in Endoscopy (EndoCV2020)
Event location:
Iowa, USA
Event start date:
2020-04-03
Event end date:
2020-04-03
EISSN:
1613-0073
ISSN:
1613-0073
Language:
English
Keywords:
Pubs id:
1106151
Local pid:
pubs:1106151
Deposit date:
2020-06-05

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