Conference item icon

Conference item

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...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


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

Authors


More by this author
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
More from this funder
Grant:
CDT in Healthcare Innovation (EP/G036861/1
Publisher:
Springer, Cham Publisher's website
Journal:
DLMIA 2017, ML-CDS 2017 Journal website
Volume:
10553
Pages:
294-302
Series:
Lecture Notes in Computer Science
Host title:
DLMIA 2017, ML-CDS 2017: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
Publication date:
2017-09-09
Acceptance date:
2017-07-10
DOI:
ISSN:
0302-9743
Source identifiers:
742579
ISBN:
9783319675589
Pubs id:
pubs:742579
UUID:
uuid:8c469567-0741-4e32-996a-86222269b1a6
Local pid:
pubs:742579
Deposit date:
2017-11-03

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP