Applications of deep learning in fundus images: A review.

Journal: Medical image analysis
Published Date:

Abstract

The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very necessary to summarize the recent developments in deep learning for fundus images with a review paper. In this review, we introduce 143 application papers with a carefully designed hierarchy. Moreover, 33 publicly available datasets are presented. Summaries and analyses are provided for each task. Finally, limitations common to all tasks are revealed and possible solutions are given. We will also release and regularly update the state-of-the-art results and newly-released datasets at https://github.com/nkicsl/Fundus_Review to adapt to the rapid development of this field.

Authors

  • Tao Li
    Department of Emergency Medicine, Jining No.1 People's Hospital, Jining, China.
  • Wang Bo
    School of Automation, Harbin University of Science and Technology, Harbin 150001, China.
  • Chunyu Hu
    College of Computer Science, Nankai University, Tianjin 300350, China.
  • Hong Kang
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Texas, USA.
  • Hanruo Liu
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Huazhu Fu
    A*STAR, Singapore, Singapore.