Medical Image Data Upscaling with Generative Adversarial Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017071" target="_blank" >RIV/62690094:18450/20:50017071 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-45385-5_66" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-45385-5_66</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-45385-5_66" target="_blank" >10.1007/978-3-030-45385-5_66</a>

Result language

angličtina

Original language name

Medical Image Data Upscaling with Generative Adversarial Networks

Original language description

Super-resolution is one of the frequently investigated methods of image processing. The quality of the results is a constant problem in the methods used to obtain high resolution images. Interpolation-based methods have blurry output problems, while non-interpolation methods require a lot of training data and high computing power. In this paper, we present a supervised generative adversarial network system that accurately generates high resolution images from a low resolution input while maintaining pathological invariance. The proposed solution is optimized for small sets of input data. Compared to existing models, our network also provides faster learning. Another advantage of our approach is its versatility for various types of medical imaging methods. We used peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as the output image quality evaluation method. The results of our test show an improvement of 5.76% compared to optimizer Adam used in the original paper [10]. For faster training of the neural network model, calculations on the graphic card with the CUDA architecture were used. © Springer Nature Switzerland AG 2020.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

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

Article name in the collection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-030-45384-8

ISSN

0302-9743

e-ISSN

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Number of pages

11

Pages from-to

739-749

Publisher name

Springer

Place of publication

Cham

Event location

Granada, Spain

Event date

May 6, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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