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Reconstructing Medical Images Using Generative Adversarial Networks: A Study

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Futuristic Trends in Networks and Computing Technologies

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 936))

Abstract

Generative adversarial networks (GANs) have been studied and utilized as an alternative to solve medical imaging problems. Major medical imaging applications include image reconstruction, denoising, segmentation, data augmentation, anomaly detection, and synthesis using image-to-image translation (I2I) techniques. There have been many notable improvements for GANs recently, and therefore, a review of notable advances when applying GANs for medical image reconstruction has been conducted for this paper. The aim of this paper is to introduce key I2I ideas and algorithms that work for medical image reconstruction applications. This study presents a review of various GAN architectures and loss functions used for medical image reconstruction that has not been done before.

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Author information

Authors and Affiliations

  1. Computer Engineering Department, Devang Patel Institute of Advance Technology and Research, Charotar University of Science and Technology (CHARUSAT), Changa, Gujarat, India

    Phenilkumar Buch

  2. Computer Science and Engineering Department, Chandubhai S Patel Institute of Technology, Charotar University of Science and Technology (CHARUSAT), Changa, Gujarat, India

    Amit Thakkar

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  1. Phenilkumar Buch
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  2. Amit Thakkar
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Correspondence to Phenilkumar Buch .

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Editors and Affiliations

  1. KIET Group of Institutions, Ghaziabad, India

    Pradeep Kumar Singh

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Sławomir T. Wierzchoń

  3. Department of Computer Engineering, NIT Kurukshetra, Haryana, India

    Jitender Kumar Chhabra

  4. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

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Buch, P., Thakkar, A. (2022). Reconstructing Medical Images Using Generative Adversarial Networks: A Study. In: Singh, P.K., Wierzchoń, S.T., Chhabra, J.K., Tanwar, S. (eds) Futuristic Trends in Networks and Computing Technologies . Lecture Notes in Electrical Engineering, vol 936. Springer, Singapore. https://doi.org/10.1007/978-981-19-5037-7_6

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  • DOI: https://doi.org/10.1007/978-981-19-5037-7_6

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  • Print ISBN: 978-981-19-5036-0

  • Online ISBN: 978-981-19-5037-7

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