A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs
Abstract Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with potential adverse side effects. Currently, there is only one alternative non-invasive system based on Optical coherence tomography (OCT) technol...
Saved in:
Main Authors: | Alireza Tavakkoli, Sharif Amit Kamran, Khondker Fariha Hossain, Stewart Lee Zuckerbrod |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2020
|
Subjects: | |
Online Access: | https://doaj.org/article/ec6869771f0945e7be893d40e0077b27 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predicting sex from retinal fundus photographs using automated deep learning
by: Edward Korot, et al.
Published: (2021) -
Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks
by: Ling-Ping Cen, et al.
Published: (2021) -
Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning
by: Diego R. Cervera, et al.
Published: (2021) -
RF-GANs: A Method to Synthesize Retinal Fundus Images Based on Generative Adversarial Network
by: Yu Chen, et al.
Published: (2021) -
Quantification of retinal blood leakage in fundus fluorescein angiography in a retinal angiogenesis model
by: Cesar H. Comin, et al.
Published: (2021)