Publication detail
Synthetic Retinal Images from Unconditional GANs
BISWAS, S. ROHDIN, J. DRAHANSKÝ, M.
Original Title
Synthetic Retinal Images from Unconditional GANs
Type
conference paper
Language
English
Original Abstract
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.
Keywords
eye retina, blood vessels, GAN, synthetic image
Authors
BISWAS, S.; ROHDIN, J.; DRAHANSKÝ, M.
Released
23. 7. 2019
Publisher
IEEE Computer Society
Location
Berlin
ISBN
978-1-5386-1311-5
Book
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society
Pages from
2736
Pages to
2739
Pages count
4
URL
BibTex
@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"
}Documents