Rapid detection of mouse spermatogenic defects by testicular cellular composition analysis via enhanced deep learning model.

Journal: Andrology
Published Date:

Abstract

BACKGROUND: Histological analysis of the testicular sections is paramount in infertility research but tedious and often requires months of training and practice.

Authors

  • Nianfei Ao
    Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
  • Min Zang
    Kuang Yaming Honors School, Nanjing University, Nanjing 210023, China.
  • Yue Lu
    Department of Gastroenterology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
  • Yiping Jiao
    Shool of Automation, Southeast University, 2nd Sipailou Road, Nanjing, China. Electronic address: ping@seu.edu.cn.
  • Haoda Lu
    School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, P.R.China;Jiangsu Key Laboratory of Large Data Analysis Technology, Nanjing 210044, P.R.China.
  • Chengfei Cai
    School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, P.R.China;Jiangsu Key Laboratory of Large Data Analysis Technology, Nanjing 210044, P.R.China.
  • Xiangxue Wang
    lnstitute for Al in Medicine, School of Artificial lntelligence, Nanjing University of Information Science and Technology, Nanjing, China.
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Minge Xie
    Department of Statistics, Rutgers University, Piscataway, New Jersey, USA.
  • Tingting Zhao
    School of Software Engineering, Beihang University, Beijing, China.
  • Jun Xu
    Department of Nephrology, The Affiliated Baiyun Hospital of Guizhou Medical University, Guizhou, China.
  • Eugene Yujun Xu
    State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, Jiangsu, China.