FunSyn-Net: Enhanced Residual Variational Auto-encoder and Image-to-Image Translation Network for Fundus Image Synthesis

Principal Investigator(s):  View help for Principal Investigator(s) Dr. Vasudevan Lakshminarayanan, University of Waterloo

Version:  View help for Version V1

Version Title:  View help for Version Title Artificial Fundus Image Dataset

Name File Type Size Last Modified
FunSyn-Net.zip application/zip 18.4 MB 01/20/2020 07:45:AM

Project Citation: 

Lakshminarayanan, Dr. Vasudevan . FunSyn-Net: Enhanced Residual Variational Auto-encoder and Image-to-Image Translation Network for Fundus Image Synthesis. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-01-20. https://doi.org/10.3886/E117290V1

Project Description

Summary:  View help for Summary This is a completely artificially generated dataset of fundus images with their corresponding blood vessel annotations.
Please use the following citation if you use the database "S. Sengupta, A. Athwale, T. Gulati, J. Zelek, V. Lakshminarayanan, FunSyn-Net: Enhanced Residual Variational Auto-encoder and Image-to-Image Translation Network for Fundus Image Synthesis, SPIE Medical Imaging, Houston, US, 2020( To be appeared)
Funding Sources:  View help for Funding Sources NSERC CANADA DISCOVERY GRANT (VL)

Scope of Project

Subject Terms:  View help for Subject Terms fundus; deep learning; retina; image synthesis; Generative Models


Related Publications

Download this project

Published Versions

V1 [2020-01-20]

Export Metadata

Dublin Core

DDI 2.5

Report a Problem

Found a serious problem with the data, such as disclosure risk or copyrighted content? Let us know.

This material is distributed exactly as it arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.