Evaluation of generative adversarial networks for high-resolution synthetic image generation of circumpapillary optical coherence tomography images for glaucoma

• Main Authors: Sreejith Kumar, Ashish Jith, Chong, Rachel S., Crowston, Jonathan G., Chua, Jacqueline, Bujor, Inna, Husain, Rahat, Vithana, Eranga N., Girard, Michaël J. A., Ting, Daniel S. W., Cheng, Ching-Yu, Aung, Tin, Popa-Cherecheanu, Alina, Schmetterer, Leopold, Wong, Damon
• Other Authors: School of Chemical and Biomedical Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Bioengineering; Glaucoma; Diagnostic Imaging
• Online Access: https://hdl.handle.net/10356/165014
• Institution: Nanyang Technological University

Importance: Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train such DL networks for glaucoma detection. Objective:...