A Weakly Supervised Deep Learning Approach for Leakage Detection in Fluorescein Angiography Images.

Journal: Translational vision science & technology
Published Date: Mar 2, 2022

Abstract

PURPOSE: The purpose of this study was to design an automated algorithm that can detect fluorescence leakage accurately and quickly without the use of a large amount of labeled data.

Authors

  • Wanyue Li
    Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou.
  • Wangyi Fang
    Department of Ophthalmology and Vision Science, Eye and ENT Hospital, Fudan University, Shanghai, People's Republic of China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Yi He
    National Institutes for Food and Drug Control, 2 Tiantan Xili, Beijing 100050, China.
  • Guohua Deng
    Department of Ophthalmology, the Third People's Hospital of Changzhou, Jiangsu, China.
  • Hong Ye
    Department of Radiation Oncology, Beaumont Health, Royal Oak, Michigan.
  • Zujun Hou
    Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, People's Republic of China.
  • Yiwei Chen
    Shanghai Synyi Medical Technology Co., Ltd, Shanghai, China.
  • Chunhui Jiang
    Department of Ophthalmology and Visual Science, Eye, Ear, Nose and Throat Hospital, Shanghai Medical College of Fudan University.
  • Guohua Shi
    Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou.

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