Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution.

Abstract

Authors

• Xiaojun Chen
  Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
• Yida Wang
  Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China. Electronic address: ydwang@phy.ecnu.edu.cn.
• Minhua Shen
  Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China.
• Bingyi Yang
  Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
• Qing Zhou
  Cardiac MR PET CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
• Yinqiao Yi
  Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China.
• Weifeng Liu
  Guangxi Key Laboratory of Pharmaceutical Precision Detection and Screening, Key Laboratory of Micro-Nanoscale Bioanalysis and Drug Screening of Guangxi Education Department, Pharmaceutical College, State Key Laboratory of Targeting Oncology, Guangxi Medical University, Nanning 530021, China.
• GuoFu Zhang
  The Affiliated Mental Health Center of Jiangnan University, Wuxi Mental Health Center, Wuxi 214151, Jiangsu, China.
• Guang Yang
  National Heart and Lung Institute, Imperial College London, London, UK.
• He Zhang
  College of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, Shaanxi, PR China; Key Laboratory of Plant Nutrition and the Agri-environment in Northwest China, Ministry of Agriculture and Rural Affairs, Yangling, 712100, Shaanxi, PR China.

Keywords

External Resources