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SpineNet: Automated classification and evidence visualization in spinal MRIs

Abstract:

The objective of this work is to automatically produce radiological gradings of spinal lumbar MRIs and also localize the predicted pathologies. We show that this can be achieved via a Convolutional Neural Network (CNN) framework that takes intervertebral disc volumes as inputs and is trained only on disc-specific class labels. Our contributions are: (i) a CNN architecture that predicts multiple gradings at once, and we propose variants of the architecture including using 3D convolutions; (ii)...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.media.2017.07.002

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Brasenose College
Role:
Author
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Grant:
EP/G036861/1
Publisher:
Elsevier Publisher's website
Journal:
Medical Image Analysis Journal website
Volume:
41
Pages:
63-73
Publication date:
2017-07-21
Acceptance date:
2017-07-20
DOI:
EISSN:
1361-8423
ISSN:
1361-8415
Pmid:
28756059
Source identifiers:
713377
Language:
English
Keywords:
Pubs id:
pubs:713377
UUID:
uuid:6caacb46-d262-4902-92cd-b9c30aee8520
Local pid:
pubs:713377
Deposit date:
2017-11-03

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