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Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer

PURPOSE: To automatically quantify colorectal tumor microenvironment (TME) in hematoxylin and eosin stained whole slide images (WSIs), and to develop a TME signature for prognostic prediction in colorectal cancer (CRC). METHODS: A deep learning model based on VGG19 architecture and transfer learning...

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Detalles Bibliográficos
Autores principales:	Shi, Liang, Zhang, Yuhao, Wang, Hong
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2023
Materias:	Medicine
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117979/ https://www.ncbi.nlm.nih.gov/pubmed/37089601 http://dx.doi.org/10.3389/fmed.2023.1154077

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