Multi-instance Deep Learning of Ultrasound Imaging Data for Pattern Classification of Congenital Abnormalities of the Kidney and Urinary Tract in Children.

Journal: Urology
Published Date:

Abstract

OBJECTIVE: To reliably and quickly diagnose children with posterior urethral valves (PUV), we developed a multi-instance deep learning method to automate image analysis.

Authors

  • Shi Yin
    School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States.
  • Qinmu Peng
    School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China; Shenzhen Huazhong University of Science and Technology Research Institute, China. Electronic address: pengqinmu@hust.edu.cn.
  • Hongming Li
    6Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Zhengqiang Zhang
    School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
  • Xinge You
    School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China; Shenzhen Huazhong University of Science and Technology Research Institute, China.
  • Katherine Fischer
    Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States; Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States.
  • Susan L Furth
    Department of Pediatrics, Division of Pediatric Nephrology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States.
  • Yong Fan
    CPB/ECMO Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.
  • Gregory E Tasian
    Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States; Center for Pediatric Clinical Effectiveness, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States; Department of Biostatistics, Epidemiology, and Informatics, The University of Pennsylvania, Philadelphia, PA, 19104, United States.