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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...
| Autores principales: | , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
John Wiley & Sons, Inc.
2020
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| Materias: | |
| 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 |