Hypothesis‐free deep survival learning applied to the tumour microenvironment in gastric cancer

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‐specifi...

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Detalles Bibliográficos
Autores principales: Meier, Armin, Nekolla, Katharina, Hewitt, Lindsay C, Earle, Sophie, Yoshikawa, Takaki, Oshima, Takashi, Miyagi, Yohei, Huss, Ralf, Schmidt, Günter, Grabsch, Heike I
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2020
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578283/
https://www.ncbi.nlm.nih.gov/pubmed/32592447
http://dx.doi.org/10.1002/cjp2.170

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7578283/
https://www.ncbi.nlm.nih.gov/pubmed/32592447
http://dx.doi.org/10.1002/cjp2.170

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