Detail publikace
Synthetic Retinal Images from Unconditional GANs
BISWAS, S. ROHDIN, J. DRAHANSKÝ, M.
Originální název
Synthetic Retinal Images from Unconditional GANs
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
eye retina, blood vessels, GAN, synthetic image
Autoři
BISWAS, S.; ROHDIN, J.; DRAHANSKÝ, M.
Vydáno
23. 7. 2019
Nakladatel
IEEE Computer Society
Místo
Berlin
ISBN
978-1-5386-1311-5
Kniha
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society
Strany od
2736
Strany do
2739
Strany počet
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"
}Dokumenty