Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes

Background The aim of this study was to investigate the influence of convolution kernel and iterative reconstruction on the diagnostic performance of radiomics and deep learning (DL) in lung adenocarcinomas. Methods A total of 183 patients with 215 lung adenocarcinomas were included in this study. A...

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Xehetasun bibliografikoak

Egile Nagusiak:	Wei Zhao, Wei Zhang, Yingli Sun, Yuxiang Ye, Jiancheng Yang, Wufei Chen, Pan Gao, Jianying Li, Cheng Li, Liang Jin, Peijun Wang, Yanqing Hua, Ming Li
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	Wiley 2019-10-01
Saila:	Thoracic Cancer
Gaiak:	Convolution kernel deep learning iterative reconstruction lung adenocarcinomas radiomics
Sarrera elektronikoa:	https://doi.org/10.1111/1759-7714.13161
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!