Medical image data upscaling with generative adversarial networks
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 trainin...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Conference or Workshop Item |
Published: |
2020
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/93945/ http://dx.doi.org/10.1007/978-3-030-45385-5_66 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.93945 |
---|---|
record_format |
eprints |
spelling |
my.utm.939452022-02-28T13:18:41Z http://eprints.utm.my/id/eprint/93945/ Medical image data upscaling with generative adversarial networks Dobrovolny, Michal Mls, Karel Krejcar, Ondrej Mambou, Sebastien Selamat, Ali T58.5-58.64 Information technology 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. 2020 Conference or Workshop Item PeerReviewed Dobrovolny, Michal and Mls, Karel and Krejcar, Ondrej and Mambou, Sebastien and Selamat, Ali (2020) Medical image data upscaling with generative adversarial networks. In: 8th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2020, 6 - 8 May 2020, Granada, Spain. http://dx.doi.org/10.1007/978-3-030-45385-5_66 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
T58.5-58.64 Information technology |
spellingShingle |
T58.5-58.64 Information technology Dobrovolny, Michal Mls, Karel Krejcar, Ondrej Mambou, Sebastien Selamat, Ali Medical image data upscaling with generative adversarial networks |
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. |
format |
Conference or Workshop Item |
author |
Dobrovolny, Michal Mls, Karel Krejcar, Ondrej Mambou, Sebastien Selamat, Ali |
author_facet |
Dobrovolny, Michal Mls, Karel Krejcar, Ondrej Mambou, Sebastien Selamat, Ali |
author_sort |
Dobrovolny, Michal |
title |
Medical image data upscaling with generative adversarial networks |
title_short |
Medical image data upscaling with generative adversarial networks |
title_full |
Medical image data upscaling with generative adversarial networks |
title_fullStr |
Medical image data upscaling with generative adversarial networks |
title_full_unstemmed |
Medical image data upscaling with generative adversarial networks |
title_sort |
medical image data upscaling with generative adversarial networks |
publishDate |
2020 |
url |
http://eprints.utm.my/id/eprint/93945/ http://dx.doi.org/10.1007/978-3-030-45385-5_66 |
_version_ |
1726791457761656832 |
score |
13.18916 |