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2019 | OriginalPaper | Buchkapitel

Generative Adversarial Networks (GANs) for Retinal Fundus Image Synthesis

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

Erschienen in: Computer Vision – ACCV 2018 Workshops

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Enhanced Detection of Referable Diabetic Retinopathy via DCNNs and Transfer Learning

Nächstes Kapitel AI-based AMD Analysis: A Review of Recent Progress

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Titel: Generative Adversarial Networks (GANs) for Retinal Fundus Image Synthesis
verfasst von: Valentina Bellemo
Philippe Burlina
Liu Yong
Tien Yin Wong
Daniel Shu Wei Ting
Verlag: Springer International Publishing
Buch: Computer Vision – ACCV 2018 Workshops
Print ISBN: 978-3-030-21073-1

Electronic ISBN: 978-3-030-21074-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-21074-8_24

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