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

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.

Objective

To study the application of anomaly detection to retinal diseases.

Design, setting, and participants

High-resolution retinal images from the publicly available EyePACS data set with fundus images with a corresponding label ranging from 0 to 4 fo

SUBMITTER: Burlina P 

PROVIDER: S-EPMC8719271 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature