A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiform...

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Detalles Bibliográficos
Autores principales: Lao, Jiangwei, Chen, Yinsheng, Li, Zhi-Cheng, Li, Qihua, Zhang, Ji, Liu, Jing, Zhai, Guangtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5583361/
https://www.ncbi.nlm.nih.gov/pubmed/28871110
http://dx.doi.org/10.1038/s41598-017-10649-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5583361/
https://www.ncbi.nlm.nih.gov/pubmed/28871110
http://dx.doi.org/10.1038/s41598-017-10649-8

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