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Prognostic risk stratification of gliomas using deep learning in digital pathology images
BACKGROUND: Evaluation of tumor-tissue images stained with hematoxylin and eosin (H&E) is pivotal in diagnosis, yet only a fraction of the rich phenotypic information is considered for clinical care. Here, we propose a survival deep learning (SDL) framework to extract this information to predict...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389424/ https://www.ncbi.nlm.nih.gov/pubmed/35990705 http://dx.doi.org/10.1093/noajnl/vdac111 |