Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks

2020 ◽  
Vol 24 (3) ◽  
pp. 855-865 ◽  
Author(s):  
Xin Yang ◽  
Yi Lin ◽  
Zhiwei Wang ◽  
Xin Li ◽  
Kwang-Ting Cheng
Keyword(s):  
Medical Image ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Review of Medical Image Synthesis using GAN Techniques

2021 ◽  
Vol 37 ◽  
pp. 01005
Author(s):  
M. Krithika alias Anbu Devi ◽  
K. Suganthi
Keyword(s):  
Medical Image ◽  
Medical Image Analysis ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
High Radiation ◽  
Adversarial Networks ◽  
Image Translation ◽  
Tomography Image ◽  
Computed Tomography Image ◽  
Single Modality

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.

scholarly journals mustGAN: multi-stream Generative Adversarial Networks for MR Image Synthesis

2021 ◽  
pp. 101944
Author(s):  
Mahmut Yurt ◽  
Salman U.H. Dar ◽  
Aykut Erdem ◽  
Erkut Erdem ◽  
Kader K Oguz ◽  
...  
Keyword(s):  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Mr Image ◽  
Adversarial Networks
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