Hard exudate detection based on deep model learned information and multi-feature joint representation for diabetic retinopathy screening.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 1, 2020

Abstract

BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR), which is generally diagnosed by the presence of hemorrhages and hard exudates, is one of the most prevalent causes of visual impairment and blindness. Early detection of hard exudates (HEs) in color fundus photographs can help in preventing such destructive damage. However, this is a challenging task due to high intra-class diversity and high similarity with other structures in the fundus images. Most of the existing methods for detecting HEs are based on characterizing HEs using hand crafted features (HCFs) only, which can not characterize HEs accurately. Deep learning methods are scarce in this domain because they require large-scale sample sets for training which are not generally available for most routine medical imaging research.

Authors

  • Hui Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Guohui Yuan
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China. Electronic address: yuanguohui@uestc.edu.cn.
  • Xuegong Zhao
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China. Electronic address: xgzhao@uestc.edu.cn.
  • Lingbing Peng
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. Electronic address: penglingbing@std.uestc.edu.cn.
  • Zhuoran Wang
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China. Electronic address: wangzhuoran@uestc.edu.cn.
  • Yanmin He
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China. Electronic address: heyanmin@uestc.edu.cn.
  • Chao Qu
    Department of Ophthalmology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu 610072, China. Electronic address: lucyjeffersonqu@hotmail.com.
  • Zhenming Peng
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 611731, China. Electronic address: zmpeng@uestc.edu.cn.

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