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Quadruple augmented pyramid network for multi-class COVID-19 segmentation via CT

Abstract:

COVID-19, a new strain of coronavirus disease, has been one of the most serious and infectious disease in the world. Chest CT is essential in prognostication, diagnosing this disease, and assessing the complication. In this paper, a multi-class COVID-19 CT segmentation is proposed aiming at helping radiologists estimate the extent of effected lung volume. We utilized four augmented pyramid networks on an encoder-decoder segmentation framework. Quadruple Augmented Pyramid Network (QAP-Net) not...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/embc46164.2021.9629904

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
Publisher:
IEEE Publisher's website
Pages:
2956-2959
Host title:
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Publication date:
2021-12-09
Acceptance date:
2021-07-16
Event title:
43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Event location:
Virtual event
Event website:
https://embc.embs.org/2021/
Event start date:
2021-11-01T00:00:00Z
Event end date:
2021-11-05T00:00:00Z
DOI:
EISBN:
978-1-7281-1179-7
EISSN:
2694-0604
ISSN:
2375-7477
Pmid:
34891865
ISBN:
978-1-7281-1180-3
Language:
English
Keywords:
Pubs id:
1240851
Local pid:
pubs:1240851
Deposit date:
2022-08-03

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