Hypothesis‐free deep survival learning applied to the tumour microenvironment in gastric cancer
Hypothesis‐free deep survival learning applied to the tumour microenvironment in gastric cancer
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Publisher
Hoboken, USA: John Wiley & Sons, Inc
Journal title
Language
English
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Publisher
Hoboken, USA: John Wiley & Sons, Inc
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Contents
The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we propose new ways to predict risk for cancer‐specific death from digital images of immunohistochemical...
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Full title
Hypothesis‐free deep survival learning applied to the tumour microenvironment in gastric cancer
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TN_cdi_doaj_primary_oai_doaj_org_article_214b0075da184485a1149f62c1e0253f
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_214b0075da184485a1149f62c1e0253f
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ISSN
2056-4538
E-ISSN
2056-4538
DOI
10.1002/cjp2.170
