DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

SIMPLE SUMMARY: Pathology images are vital for understanding solid cancers. In this study, we created DeepRePath using multi-scale pathology images with two-channel deep learning to predict the prognosis of patients with early-stage lung adenocarcinoma (LUAD). DeepRePath demonstrated that it could p...

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Detalles Bibliográficos
Autores principales: Shim, Won Sang, Yim, Kwangil, Kim, Tae-Jung, Sung, Yeoun Eun, Lee, Gyeongyun, Hong, Ji Hyung, Chun, Sang Hoon, Kim, Seoree, An, Ho Jung, Na, Sae Jung, Kim, Jae Jun, Moon, Mi Hyoung, Moon, Seok Whan, Park, Sungsoo, Hong, Soon Auck, Ko, Yoon Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268823/
https://www.ncbi.nlm.nih.gov/pubmed/34282757
http://dx.doi.org/10.3390/cancers13133308

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8268823/
https://www.ncbi.nlm.nih.gov/pubmed/34282757
http://dx.doi.org/10.3390/cancers13133308

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