Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases.

ABSTRACT:

Importance

Recent studies have demonstrated the successful application of artificial intelligence (AI) for automated retinal disease diagnostics but have not addressed a fundamental challenge for deep learning systems: the current need for large, criterion standard-annotated retinal data sets for training. Low-shot learning algorithms, aiming to learn from a relatively low number of training data, may be beneficial for clinical situations involving rare retinal diseases or when addressing potential bias resulting from data that may not adequately represent certain groups for training, such as individuals older than 85 years.

Objective

To evaluate whether low-shot deep learning methods are beneficial when using small training data sets for automated retinal diagnostics.

De

SUBMITTER: Burlina P 

PROVIDER: S-EPMC7489388 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature