Generative Adversarial Networks (GANs) for Retinal Fundus Image Synthesis
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 11367 LNCS, Page: 289-302
2019
- 26Citations
- 48Captures
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Conference Paper Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85068487313&origin=inward; http://dx.doi.org/10.1007/978-3-030-21074-8_24; http://link.springer.com/10.1007/978-3-030-21074-8_24; http://link.springer.com/content/pdf/10.1007/978-3-030-21074-8_24; https://doi.org/10.1007%2F978-3-030-21074-8_24; https://dx.doi.org/10.1007/978-3-030-21074-8_24; https://link.springer.com/chapter/10.1007/978-3-030-21074-8_24
Springer Science and Business Media LLC
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