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Self-supervised learning for spinal MRIs

Abstract:

A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of “free” self-supervision for training a deep network. We demonstrate this self-supervised learning for the case of T2-weighted sagittal lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network (CNN) is trained using two losses: (i) a contrastive loss on whether the scan is of the same person (i.e. longit...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1007/978-3-319-67558-9_34

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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 Division
Department:
Engineering Science
Oxford college:
Brasenose College
Role:
Author
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Grant:
CDT in Healthcare Innovation (EP/G036861/1)
Publisher:
Springer, Cham Publisher's website
Volume:
10553
Pages:
294-302
Series:
Lecture Notes in Computer Science
Publication date:
2017-09-09
Acceptance date:
2017-07-10
DOI:
ISSN:
0302-9743
Pubs id:
pubs:742579
URN:
uri:8c469567-0741-4e32-996a-86222269b1a6
UUID:
uuid:8c469567-0741-4e32-996a-86222269b1a6
Local pid:
pubs:742579
ISBN:
978-3-319-67558-9

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