An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning.

Journal: Translational vision science & technology
PMID: 40048172

Abstract

PURPOSE: To develop an intelligent grading model for myopic maculopathy based on a long-tail learning framework, using the improved loss function LTBSoftmax. The model addresses the long-tail distribution problem in myopic maculopathy data to provide preliminary grading, aiming to improve grading capability and efficiency.

Authors

  • Bo Zheng
    State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
  • Chen Wang
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Maotao Zhang
    School of Information Engineering, Huzhou University, Huzhou, China.
  • Shaojun Zhu
    School of Information Engineering, Huzhou University, Huzhou 313000, China.
  • Maonian Wu
    School of Information Engineering, Huzhou University, Huzhou 313000, China.
  • Tao Wu
    Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Weihua Yang
    Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, Guangdong Province, China.
  • Lu Chen
    Ultrasonic Department, Zhongda Hospital Affiliated to Southeast University, Nanjing, 210009, China.

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