Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization

arXiv:2203.13856v2 Announce Type: replace-cross
Abstract: Deep learning applications for assessing medical images are limited because the datasets are often small and imbalanced. The use of synthetic data has been proposed in the literature, but neither a robust comparison of the different methods nor generalizability has been reported. Our approach integrates a retinal image quality assessment model and StyleGAN2 architecture to enhance Age-related Macular Degeneration (AMD) detection capabilities and improve generalizability. This work compares ten different Generative Adversarial Network (GAN) architectures …

abstract applications arxiv comparison cs.cv cs.lg data datasets deep learning eess.iv images literature medical robust small synthetic synthetic data type