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

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

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:...

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Bibliographic Details
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: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Bioengineering
Glaucoma
Diagnostic Imaging
Online Access:https://hdl.handle.net/10356/165014

Internet

https://hdl.handle.net/10356/165014

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