Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning

To develop a deep learning system based on 3D convolutional neural networks (CNNs), and to automatically predict EGFR‐mutant pulmonary adenocarcinoma in CT images. A dataset of 579 nodules with EGFR mutation status labels of mutant (Mut) or wild‐type (WT) was retrospectively analyzed. A deep learnin...

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Detalles Bibliográficos
Autores principales: Zhao, Wei, Yang, Jiancheng, Ni, Bingbing, Bi, Dexi, Sun, Yingli, Xu, Mengdi, Zhu, Xiaoxia, Li, Cheng, Jin, Liang, Gao, Pan, Wang, Peijun, Hua, Yanqing, Li, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Cancer Biology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6601587/
https://www.ncbi.nlm.nih.gov/pubmed/31074592
http://dx.doi.org/10.1002/cam4.2233

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6601587/
https://www.ncbi.nlm.nih.gov/pubmed/31074592
http://dx.doi.org/10.1002/cam4.2233

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