Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi-Parametric MRI by Deep Learning.

Abstract

Authors

• Yida Wang
  Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China. Electronic address: ydwang@phy.ecnu.edu.cn.
• Wei Liu
  Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.
• Yuanyuan Lu
  Department of Radiology, Shanghai First Maternity and Infant Health Hospital, School of Medicine, Tongji University, Shanghai, China.
• Rennan Ling
  Department of Radiology, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shanghai, China.
• Wenjing Wang
  School of Economics, Tianjin University of Commerce, Tianjin, 300134, China. Electronic address: maggiewwj@163.com.
• Shengyong Li
  Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China.
• Feiran Zhang
  Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
• Yan Ning
  Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
• Xiaojun Chen
  Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 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