Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network.

Yang, Yingjian; Wang, Shicong; Zeng, Nanrong; Duan, Wenxin; Chen, Ziran; Liu, Yang; Li, Wei; Guo, Yingwei; Chen, Huai; Li, Xian; Chen, Rongchang; Kang, Yan

Yang, Yingjian; Wang, Shicong; Zeng, Nanrong; Duan, Wenxin; Chen, Ziran; Liu, Yang; Li, Wei; Guo, Yingwei; Chen, Huai; Li, Xian; Chen, Rongchang; Kang, Yan.

Afiliación

Yang Y; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
Wang S; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
Zeng N; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
Duan W; School of Applied Technology, Shenzhen University, Shenzhen 518060, China.
Chen Z; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
Liu Y; School of Applied Technology, Shenzhen University, Shenzhen 518060, China.
Li W; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
Guo Y; School of Applied Technology, Shenzhen University, Shenzhen 518060, China.
Chen H; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
Li X; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
Chen R; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China.
Kang Y; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.

Diagnostics (Basel) ; 12(10)2022 Sep 20.

Article en En | MEDLINE | ID: mdl-36291964

Palabras clave

COPD stage (GOLD); Lasso algorithm; auto-metric graph neural network (AMGNN); chest HRCT image; generalized linear model (GLM); lung radiomics features; multi-classification

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagnostics (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google