Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration.

Journal: JAMA ophthalmology
Published Date: Mar 1, 2019

Abstract

IMPORTANCE: Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degeneration (AMD), requires large image data sets graded by human experts to train deep convolutional neural networks (DCNNs). In contrast, generative DL techniques could synthesize large new data sets of artificial retina images with different stages of AMD. Such images could enhance existing data sets of common and rare ophthalmic diseases without concern for personally identifying information to assist medical education of students, residents, and retinal specialists, as well as for training new DL diagnostic models for which extensive data sets from large clinical trials of expertly graded images may not exist.

Authors

  • Philippe M Burlina
    The Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland.
  • Neil Joshi
    Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America.
  • Katia D Pacheco
    Retina Division, Brazilian Center of Vision Eye Hospital, DF, Brazil.
  • T Y Alvin Liu
    Wilmer Eye Institute at the Johns Hopkins University School of Medicine, Baltimore, MD.
  • Neil M Bressler
    Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland4Editor, JAMA Ophthalmology.

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