Generative Adversarial Networks (GANs) for Retinal Fundus Image Synthesis

Valentina Bellemo, Philippe Burlina, Liu Yong, Tien Yin Wong, Daniel Shu Wei Ting

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The lack of access to large annotated datasets and legal concerns regarding patient privacy are limiting factors for many applications of deep learning in the retinal image analysis domain. Therefore the idea of generating synthetic retinal images, indiscernible from real data, has gained more interest. Generative adversarial networks (GANs) have proven to be a valuable framework for producing synthetic databases of anatomically consistent retinal fundus images. In Ophthalmology, GANs in particular have shown increased interest. We discuss here the potential advantages and limitations that need to be addressed before GANs can be widely adopted for retinal imaging.

Original languageEnglish (US)
Title of host publicationComputer Vision – ACCV 2018 Workshops - 14th Asian Conference on Computer Vision, 2018, Revised Selected Papers
EditorsGustavo Carneiro, Shaodi You
PublisherSpringer Verlag
Pages289-302
Number of pages14
ISBN (Print)9783030210731
DOIs
StatePublished - Jan 1 2019
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: Dec 2 2018Dec 6 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11367 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Asian Conference on Computer Vision, ACCV 2018
CountryAustralia
CityPerth
Period12/2/1812/6/18

Keywords

  • Deep learning
  • Generative adversarial networks
  • Medical imaging
  • Retinal fundus images
  • Survey

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Generative Adversarial Networks (GANs) for Retinal Fundus Image Synthesis'. Together they form a unique fingerprint.

Cite this