Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning.

Journal: JAMA ophthalmology
Published Date: Feb 1, 2022

Abstract

IMPORTANCE: Anomaly detectors could be pursued for retinal diagnoses based on artificial intelligence systems that may not have access to training examples for all retinal diseases in all phenotypic presentations. Possible applications could include screening of population for any retinal disease rather than a specific disease such as diabetic retinopathy, detection of novel retinal diseases or novel presentations of common retinal diseases, and detection of rare diseases with little or no data available for training.

Authors

  • Philippe Burlina
    Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America.
  • William Paul
    Applied Physics Laboratory, The Johns Hopkins University, Baltimore, Maryland.
  • 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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