Synthesizing realistic high-resolution retina image by style-based generative adversarial network and its utilization.

ABSTRACT: Realistic image synthesis based on deep learning is an invaluable technique for developing high-performance computer aided diagnosis systems while protecting patient privacy. However, training a generative adversarial network (GAN) for image synthesis remains challenging because of the large amounts of data required for training various kinds of image features. This study aims to synthesize retinal images indistinguishable from real images and evaluate the efficacy of the synthesized images having a specific disease for augmenting class imbalanced datasets. The synthesized images were validated via image Turing tests, qualitative analysis by retinal specialists, and quantitative analyses on amounts and signal-to-noise ratios of vessels. The efficacy of synthesized images was verified by de

SUBMITTER: Kim M 

PROVIDER: S-EPMC9569369 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature