Cargando…
Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network
OBJECTIVES: This study aimed to establish and validate a prognostic model based on magnetic resonance imaging and clinical features to predict the survival time of patients with glioblastoma multiforme (GBM). METHODS: In this study, a convolutional denoising autoencoder (DAE) network combined with t...
Autores principales: | Yan, Ting, Yan, Zhenpeng, Liu, Lili, Zhang, Xiaoyu, Chen, Guohui, Xu, Feng, Li, Ying, Zhang, Lijuan, Peng, Meilan, Wang, Lu, Li, Dandan, Zhao, Dong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9871481/ https://www.ncbi.nlm.nih.gov/pubmed/36704230 http://dx.doi.org/10.3389/fncom.2022.916511 |
Ejemplares similares
-
Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder
por: Li, Zexin, et al.
Publicado: (2020) -
Tests for the proportional hazards assumption in the Cox model
por: Reiersølmoen, L, et al.
Publicado: (2001) -
On Cox proportional hazards model performance under different sampling schemes
por: Samawi, Hani, et al.
Publicado: (2023) -
Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models
por: Emura, Takeshi, et al.
Publicado: (2012) -
Sparse Convolutional Denoising Autoencoders for Genotype Imputation
por: Chen, Junjie, et al.
Publicado: (2019)