Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinic...

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Detalles Bibliográficos
Autores principales: Diao, James A., Wang, Jason K., Chui, Wan Fung, Mountain, Victoria, Gullapally, Sai Chowdary, Srinivasan, Ramprakash, Mitchell, Richard N., Glass, Benjamin, Hoffman, Sara, Rao, Sudha K., Maheshwari, Chirag, Lahiri, Abhik, Prakash, Aaditya, McLoughlin, Ryan, Kerner, Jennifer K., Resnick, Murray B., Montalto, Michael C., Khosla, Aditya, Wapinski, Ilan N., Beck, Andrew H., Elliott, Hunter L., Taylor-Weiner, Amaro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955068/
https://www.ncbi.nlm.nih.gov/pubmed/33712588
http://dx.doi.org/10.1038/s41467-021-21896-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955068/
https://www.ncbi.nlm.nih.gov/pubmed/33712588
http://dx.doi.org/10.1038/s41467-021-21896-9

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