Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer Learning-Reference-Cited by-同舟云学术

Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer Learning

Published:2023-09 Issue:9 Volume:42 Page:2616-2630
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Li Ming¹^ORCID,Wang Jiping²^ORCID,Chen Yang³^ORCID,Tang Yufei¹,Wu Zhongyi¹^ORCID,Qi Yujin⁴^ORCID,Jiang Haochuan⁵,Zheng Jian¹^ORCID,Tsui Benjamin M. W.⁶^ORCID

Affiliation:

1. Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China

2. Institute of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, China

3. Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, School of Computer Science and Engineering, Ministry of Education, Southeast University, Nanjing, China

4. Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia

5. Minfound Medical Systems Company Ltd., Yuecheng, Zhejiang, Shaoxing, China

6. Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

Funder

National Key Research and Development Program of China

National Major Scientific Research Instrument Development Project

Natural Science Foundation of China

Key Research and Development Programs in Jiangsu Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10236920/10081080.pdf?arnumber=10081080

Reference53 articles.

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5. GANs trained by a two time-scale update rule converge to a local Nash equilibrium;heusel;Proc NIPS,2017

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