Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.

ABSTRACT: Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network (GAN) can improve the applicability of DL in the optical coherence tomography (OCT) diagnosis of rare diseases. Four major classes with a large number of datasets and five rare disease classes with a few-shot dataset are included in this study. Before training the classifier, we constructed GAN models to generate pathological OCT images of each rare disease from normal OCT images. The Inception-v3 architecture was trained using an augmented training dataset, and the final model was validated using an independent test dataset. The synthetic ima

SUBMITTER: Yoo TK 

PROVIDER: S-EPMC7829497 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature