mlsanityData to 7 Oct 2026

Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Review

arXiv 1812.07101 ↗First posted 9 Dec 2018cs.CV cs.LG stat.MLcited by 11

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Abstract (arXiv)

An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma,diabetic macular edema and diabetic retinopathy are also reported.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Dec 2018.

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Citing papers per year: 2021: 7; 2022: 1; 2023: 3.

11 papers.

  1. 2023A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervisioncited by 78
  2. 2021Applications of Deep Learning in Fundus Images: A Reviewcited by 59
  3. 2021A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detectioncited by 34
  4. 2023PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotationcited by 11
  5. 2021Uncertainty aware and explainable diagnosis of retinal diseasecited by 7
  6. 2021The Three-Dimensional Structural Configuration of the Central Retinal Vessel Trunk and Branches as a Glaucoma Biomarkercited by 4
  7. 2021Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Surveycited by 2
  8. 2021MRI Images, Brain Lesions and Deep Learningcited by 1
  9. 2021Rapid Classification of Glaucomatous Fundus Imagescited by 0
  10. 2022Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Imagescited by 0
  11. 2023Eye tracking guided deep multiple instance learning with dual cross-attention for fundus disease detectioncited by 0