Publication Details
Synthetic Retinal Images from Unconditional GANs
Rohdin Johan Andréas, M.Sc., Ph.D. (DCGM)
Drahanský Martin, prof. Ing., Ph.D.
eye retina, blood vessels, GAN, synthetic image
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.
@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"
}