Synthetic Retinal Images from Unconditional GANs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU135167" target="_blank" >RIV/00216305:26230/19:PU135167 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8857857" target="_blank" >https://ieeexplore.ieee.org/document/8857857</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EMBC.2019.8857857" target="_blank" >10.1109/EMBC.2019.8857857</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Synthetic Retinal Images from Unconditional GANs

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Synthetic Retinal Images from Unconditional GANs

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society

ISBN

978-1-5386-1311-5

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

2736-2739

Název nakladatele

IEEE Computer Society

Místo vydání

Berlin

Místo konání akce

Berlin

Datum konání akce

23. 7. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000557295303038