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International Work-Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2020: Bioinformatics and Biomedical Engineering pp 739–749Cite as

Medical Image Data Upscaling with Generative Adversarial Networks

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12108))

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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Acknowledgement

The work and the contribution were supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic.

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Authors and Affiliations

  1. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Hradec Kralove, Czech Republic

    Michal Dobrovolny, Karel Mls, Ondrej Krejcar, Sebastien Mambou & Ali Selamat

  2. Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Ali Selamat

  3. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Malaysia

    Ali Selamat

Authors
  1. Michal Dobrovolny
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  2. Karel Mls
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  3. Ondrej Krejcar
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  4. Sebastien Mambou
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  5. Ali Selamat
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Corresponding author

Correspondence to Ondrej Krejcar .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Olga Valenzuela

  3. University of Granada, Granada, Spain

    Fernando Rojas

  4. University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Chicago and Fundacion Progreso y Salud, Granada, Spain

    Francisco Ortuño

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Cite this paper

Dobrovolny, M., Mls, K., Krejcar, O., Mambou, S., Selamat, A. (2020). Medical Image Data Upscaling with Generative Adversarial Networks. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_66

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  • DOI: https://doi.org/10.1007/978-3-030-45385-5_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45384-8

  • Online ISBN: 978-3-030-45385-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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