Multi-label classification of pelvic organ prolapse using stress magnetic resonance imaging with deep learning.

Journal: International urogynecology journal
Published Date: Jan 27, 2022

Abstract

INTRODUCTION AND HYPOTHESIS: We aimed to develop a deep learning-based multi-label classification model to simultaneously diagnose three types of pelvic organ prolapse using stress magnetic resonance imaging (MRI).

Authors

  • Xinyi Wang
    School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.
  • Da He
    Department of Spine Surgery, Peking University Fourth School of Clinical Medicine and Beijing Jishuitan Hospital, Beijing, China.
  • Fei Feng
    Department of Mathematical, Yunnan Normal University, Kunming 650092, People's Republic of China.
  • James A Ashton-Miller
    Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
  • John O L DeLancey
    Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Jiajia Luo
    Biomedical Engineering Department, Peking University, Beijing, 100191, China. jiajia.luo@pku.edu.cn.

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