Discrimination between transient and persistent subsolid pulmonary nodules on baseline CT using deep transfer learning.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To develop and validate a deep learning model to discriminate transient from persistent subsolid nodules (SSNs) on baseline CT.

Authors

  • Chuxi Huang
    Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, No.305, Zhongshan East Road, Nanjing, 210002, China.
  • Wenhui Lv
    Department of Medical Imaging, Jinling Hospital, Southern Medical University, No.305, Zhongshan East Road, Nanjing, 210002, China.
  • Changsheng Zhou
    Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, No.305, Zhongshan East Road, Nanjing, 210002, China.
  • Li Mao
    Deepwise AI Lab, Deepwise Inc, No.8 Haidian avenue, Sinosteel International Plaza, Beijing, 100080, China.
  • Qinmei Xu
    Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, No.305, Zhongshan East Road, Nanjing, 210002, China.
  • Xinyu Li
    School of Pharmacy, Binzhou Medical University, Yantai, China.
  • Li Qi
    Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, No.305, Zhongshan East Road, Nanjing, 210002, China.
  • Fei Xia
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
  • Xiuli Li
    Department of Obstetrics and Gynecology, General Hospital of Chinese People's Liberation Army, Beijing 100853, China.
  • Qirui Zhang
    Department of Medical Imaging, Jinling Hospital, Southern Medical University, No.305, Zhongshan East Road, Nanjing, 210002, China.
  • Longjiang Zhang
    Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, No.305, Zhongshan East Road, Nanjing, 210002, China.
  • Guangming Lu
    Department of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.