Generative Adversarial Networks (GANs) for Retinal Fundus Image Synthesis
| Autor: | Philippe Burlina, Daniel Shu Wei Ting, Liu Yong, Valentina Bellemo, Tien Yin Wong |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Deep learning Fundus image ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Retinal Machine learning computer.software_genre Retinal image 030218 nuclear medicine & medical imaging Domain (software engineering) 03 medical and health sciences chemistry.chemical_compound Adversarial system 0302 clinical medicine chemistry 030221 ophthalmology & optometry Retinal imaging Artificial intelligence business computer Generative grammar |
| Zdroj: | Computer Vision – ACCV 2018 Workshops ISBN: 9783030210731 ACCV Workshops |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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