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

Abstract

Authors

• Wei Zhao
  Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.
• Wei Zhang
  The First Affiliated Hospital of Nanchang University, Nanchang, China.
• Yingli Sun
  Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
• Yuxiang Ye
  Diannei Technology, Shanghai, China.
• Jiancheng Yang
  Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China.
• Wufei Chen
  Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
• Pan Gao
  College of Information Science and Technology, Shihezi University, Shihezi 832003, China.
• Jianying Li
  CT Research Center, GE Healthcare China, Beijing 100176, China.
• Cheng Li
  College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, China.
• Liang Jin
  Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.
• Peijun Wang
  Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, P.R. China.
• Yanqing Hua
  Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, P.R. China. minli77@163.com huayq007@163.com.
• Ming Li
  Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, China.

Keywords

External Resources