Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer
Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer
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Author / Creator
Mercan, Caner , Balkenhol, Maschenka , Salgado, Roberto , Sherman, Mark , Vielh, Philippe , Vreuls, Willem , Polónia, António , Horlings, Hugo M. , Weichert, Wilko , Carter, Jodi M. , Bult, Peter , Christgen, Matthias , Denkert, Carsten , van de Vijver, Koen , Bokhorst, John-Melle , van der Laak, Jeroen and Ciompi, Francesco
Publisher
London: Nature Publishing Group UK
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English
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Publisher
London: Nature Publishing Group UK
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Contents
To guide the choice of treatment, every new breast cancer is assessed for aggressiveness (i.e., graded) by an experienced histopathologist. Typically, this tumor grade consists of three components, one of which is the nuclear pleomorphism score (the extent of abnormalities in the overall appearance of tumor nuclei). The degree of nuclear pleomorphi...
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Full title
Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer
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TN_cdi_doaj_primary_oai_doaj_org_article_d5b7d6478e724a4fa2ba82b4cac30d1c
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d5b7d6478e724a4fa2ba82b4cac30d1c
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ISSN
2374-4677
E-ISSN
2374-4677
DOI
10.1038/s41523-022-00488-w
