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Deep learning for detecting tumour infiltrating lymphocytes in testicular germ cell tumors

Abstract:
Aims To evaluate if a deep learning algorithm can be trained to identify tumour-infiltrating lymphocytes (TILs) in tissue samples of testicular germ cell tumours and to assess whether the TIL counts correlate with relapse status of the patient.

Methods TILs were manually annotated in 259 tumour regions from 28 whole-slide images (WSIs) of H&E-stained; tissue samples. A deep learning algorithm was trained on half of the regions and tested on the... Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1136/jclinpath-2018-205328

Authors


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Institution:
University of Oxford
Division:
NDM
Department:
Wellcome Trust Centre for Human Genetics
Oxford college:
St Edmund Hall
Role:
Author
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Institution:
University of Oxford
Division:
RDM
Department:
RDM Clinical Laboratory Sciences
Role:
Author
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Oncology
Role:
Author
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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Medical School
Role:
Author
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More from this funder
Funding agency for:
Verrill, C
Grant:
Biomedical Research Centre
National Institute for Health Research More from this funder
Publisher:
BMJ Publishing Group Publisher's website
Journal:
Journal of Clinical Pathology Journal website
Volume:
72
Issue:
2
Publication date:
2018-12-05
Acceptance date:
2018-11-03
DOI:
EISSN:
1472-4146
ISSN:
0021-9746
Source identifiers:
944874
Pubs id:
pubs:944874
UUID:
uuid:7dd6ad79-25a4-4f64-801f-d4791b43386f
Local pid:
pubs:944874
Deposit date:
2018-11-21

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