Optimization of cardiac magnetic resonance synthetic image based on simulated generative adversarial network

Fu, Y., Gong, M., Yang, G., Hu, J., Wei, H. ORCID: https://orcid.org/0000-0002-9664-5748 and Zhou, J. (2021) Optimization of cardiac magnetic resonance synthetic image based on simulated generative adversarial network. Mathematical Problems in Engineering, 2021 (1). 3279563. ISSN 1024-123X doi: 10.1155/2021/3279563

Abstract/Summary

The generative adversarial network (GAN) has advantage to fit data distribution, so it can achieve data augmentation by fitting the real distribution and synthesizing additional training data. In this way, the deep convolution model can also be well trained in the case of using a small sample medical image data set. However, some certain gaps still exist between synthetic images and real images. In order to further narrow those gaps, this paper proposed a method that applies SimGAN on cardiac magnetic resonance synthetic image optimization task. Meanwhile, the improved residual structure is used to deepen the network structure to improve the performance of the optimizer. Lastly, the experiments will show the good result of our data augmentation method based on GAN.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/119721
Identification Number/DOI	10.1155/2021/3279563
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Hindawi Publishing
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