ITM Web Conf.
Volume 37, 2021International Conference on Innovative Technology for Sustainable Development (ICITSD-2021)
|Number of page(s)||8|
|Section||Innovative Technology for Sustainable Development|
|Published online||17 March 2021|
Review of Medical Image Synthesis using GAN Techniques
School of Electronics Engineering,Vellore Institute of Technology, Chennai
* Corresponding author: firstname.lastname@example.org
Generative Adversarial Networks (GANs) is one of the vital efficient methods for generating a massive, high-quality artificial picture. For diagnosing particular diseases in a medical image, a general problem is that it is expensive, usage of high radiation dosage, and time-consuming to collect data. Hence GAN is a deep learning method that has been developed for the image to image translation, i.e. from low-resolution to highresolution image, for example generating Magnetic resonance image (MRI) from computed tomography image (CT) and 7T from 3T MRI which can be used to obtain multimodal datasets from single modality. In this review paper, different GAN architectures were discussed for medical image analysis.
Key words: Generative adversarial network / Computerized Tomography / Magnetic Resonance Imaging
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.