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2020 | OriginalPaper | Chapter

Medical Image Data Upscaling with Generative Adversarial Networks

Authors : Michal Dobrovolny, Karel Mls, Ondrej Krejcar, Sebastien Mambou, Ali Selamat

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

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Abstract

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.

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Title: Medical Image Data Upscaling with Generative Adversarial Networks
Authors: Michal Dobrovolny
Karel Mls
Ondrej Krejcar
Sebastien Mambou
Ali Selamat
Publisher: Springer International Publishing
Book: Bioinformatics and Biomedical Engineering
Print ISBN: 978-3-030-45384-8

Electronic ISBN: 978-3-030-45385-5

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-45385-5_66

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