Predicting cancer outcomes from histology and genomics using convolutional networks

Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine...

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Detalles Bibliográficos
Autores principales: Mobadersany, Pooya, Yousefi, Safoora, Amgad, Mohamed, Gutman, David A., Barnholtz-Sloan, Jill S., Velázquez Vega, José E., Brat, Daniel J., Cooper, Lee A. D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2018
Materias:
PNAS Plus
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5879673/
https://www.ncbi.nlm.nih.gov/pubmed/29531073
http://dx.doi.org/10.1073/pnas.1717139115

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5879673/
https://www.ncbi.nlm.nih.gov/pubmed/29531073
http://dx.doi.org/10.1073/pnas.1717139115

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