DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

Histopathological whole slide images of haematoxylin and eosin (H&E)-stained biopsies contain valuable information with relation to cancer disease and its clinical outcomes. Still, there are no highly accurate automated methods to correlate histolopathological images with brain cancer patients’...

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Detalles Bibliográficos
Autores principales: Zadeh Shirazi, Amin, Fornaciari, Eric, Bagherian, Narjes Sadat, Ebert, Lisa M., Koszyca, Barbara, Gomez, Guillermo A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7188709/
https://www.ncbi.nlm.nih.gov/pubmed/32124225
http://dx.doi.org/10.1007/s11517-020-02147-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7188709/
https://www.ncbi.nlm.nih.gov/pubmed/32124225
http://dx.doi.org/10.1007/s11517-020-02147-3

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