Semi-automated analysis of digital whole slides from humanized lung-cancer xenograft models for checkpoint inhibitor response prediction

We propose a deep learning workflow for the classification of hematoxylin and eosin stained histological whole-slide images of non-small-cell lung cancer. The workflow includes automatic extraction of meta-features for the characterization of the tumor. We show that the tissue-classification produce...

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Detalles Bibliográficos
Autores principales: Bug, Daniel, Feuerhake, Friedrich, Oswald, Eva, Schüler, Julia, Merhof, Dorit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2019
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642041/
https://www.ncbi.nlm.nih.gov/pubmed/31360306
http://dx.doi.org/10.18632/oncotarget.27069

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642041/
https://www.ncbi.nlm.nih.gov/pubmed/31360306
http://dx.doi.org/10.18632/oncotarget.27069

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