Journal article
Deep learning for detecting tumour infiltrating lymphocytes in testicular germ cell tumors
- Abstract:
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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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- Files:
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(Accepted manuscript, pdf, 439.4KB)
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- Publisher copy:
- 10.1136/jclinpath-2018-205328
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Authors
Funding
+ National Institute for Health Research
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Funding agency for:
Verrill, C
Grant:
Biomedical Research Centre
National Institute for Health Research
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Bibliographic Details
- 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:
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1472-4146
- ISSN:
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0021-9746
- Source identifiers:
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944874
Item Description
- Pubs id:
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pubs:944874
- UUID:
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uuid:7dd6ad79-25a4-4f64-801f-d4791b43386f
- Local pid:
- pubs:944874
- Deposit date:
- 2018-11-21
Terms of use
- Copyright holder:
- Linder et al
- Copyright date:
- 2018
- Notes:
- © Author(s) (or their employer(s)) 2018. No commercial re-use. This is the accepted manuscript version of the article. The final version is available online from BMJ Publishing Group at: 10.1136/jclinpath-2018-205328
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