Assessing the Efficacy of Synthetic Optic Disc Images for Detecting Glaucomatous Optic Neuropathy Using Deep Learning.

Journal: Translational vision science & technology
Published Date: Jun 3, 2024

Abstract

PURPOSE: Deep learning architectures can automatically learn complex features and patterns associated with glaucomatous optic neuropathy (GON). However, developing robust algorithms requires a large number of data sets. We sought to train an adversarial model for generating high-quality optic disc images from a large, diverse data set and then assessed the performance of models on generated synthetic images for detecting GON.

Authors

  • Abadh K Chaurasia
    Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Tasmania.
  • Stuart MacGregor
    Statistical Genetics Lab, QIMR Berghofer Medical Research Institute, Brisbane, Herston, QLD 4006, Australia.
  • Jamie E Craig
    Department of Ophthalmology, Flinders University, Flinders Medical Centre, Bedford Park, SA 5042, Australia.
  • David A Mackey
    Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia.
  • Alex W Hewitt
    Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia.

Keywords

External Resources

Popular Topics
Recent Journals