Retinal image synthesis from multiple-landmarks input with generative adversarial networks.
Journal:
Biomedical engineering online
Published Date:
May 21, 2019
Abstract
BACKGROUND: Medical datasets, especially medical images, are often imbalanced due to the different incidences of various diseases. To address this problem, many methods have been proposed to synthesize medical images using generative adversarial networks (GANs) to enlarge training datasets for facilitating medical image analysis. For instance, conventional methods such as image-to-image translation techniques are used to synthesize fundus images with their respective vessel trees in the field of fundus image.
