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Automated annotator: capturing expert knowledge for free

Abstract:

Deep learning enabled medical image analysis is heavily reliant on expert annotations which is costly. We present a simple yet effective automated annotation pipeline that uses autoencoder based heatmaps to exploit high level information that can be extracted from a histology viewer in an unobtrusive fashion. By predicting heatmaps on unseen images the model effectively acts like a robot annotator. The method is demonstrated in the context of coeliac disease histology images in this initial w...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/embc46164.2021.9630309

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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
NDM Experimental Medicine
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-8528-8298
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Publisher:
IEEE Publisher's website
Pages:
2664-2667
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 (EMBC)
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:
9781728111797
EISSN:
2694-0604
ISSN:
2375-7477
Pmid:
34891800
ISBN:
9781728111803
Language:
English
Keywords:
Pubs id:
1230582
Local pid:
pubs:1230582
Deposit date:
2022-08-02

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