Deep learned tissue “fingerprints” classify breast cancers by ER/PR/Her2 status from H&E images

Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&E features...

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Detalles Bibliográficos
Autores principales: Rawat, Rishi R., Ortega, Itzel, Roy, Preeyam, Sha, Fei, Shibata, Darryl, Ruderman, Daniel, Agus, David B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190637/
https://www.ncbi.nlm.nih.gov/pubmed/32350370
http://dx.doi.org/10.1038/s41598-020-64156-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190637/
https://www.ncbi.nlm.nih.gov/pubmed/32350370
http://dx.doi.org/10.1038/s41598-020-64156-4

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