Result Details
Synthetic Retinal Images from Unconditional GANs
Rohdin Johan Andréas, M.Sc., Ph.D., FIT (FIT), DCGM (FIT)
Drahanský Martin, prof. Ing., Ph.D., DITS (FIT)
Synthesized retinal images are highly demanded in the development of automated eye applications since they can make machine learning algorithms more robust by increasing the size and heterogeneity of the training database. Recently, conditional Generative Adversarial Networks (cGANs) based synthesizers have been shown to be promising for generating retinal images. However, cGANs based synthesizers require segmented blood vessels (BV) along with RGB retinal images during training. The amount of such data (i.e., retinal images and their corresponding BV) available in public databases is very small. Therefore, for training cGANs, an extra system is necessary either for synthesizing BV or for segmenting BV from retinal images. In this paper, we show that by using unconditional GANs (uGANs) we can generate synthesized
retinal images without using BV images.
eye retina, blood vessels, GAN, synthetic image
@inproceedings{BUT161844,
author="Sangeeta {Biswas} and Johan Andréas {Rohdin} and Martin {Drahanský}",
title="Synthetic Retinal Images from Unconditional GANs",
booktitle="Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society",
year="2019",
pages="2736--2739",
publisher="IEEE Computer Society",
address="Berlin",
doi="10.1109/EMBC.2019.8857857",
isbn="978-1-5386-1311-5",
url="https://ieeexplore.ieee.org/document/8857857"
}IT4Innovations excellence in science, MŠMT, Národní program udržitelnosti II, LQ1602, start: 2016-01-01, end: 2020-12-31, completed