Low‐dose CT denoising with a high‐level feature refinement and dynamic convolution network-Reference-Cited by-同舟云学术

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Low‐dose CT denoising with a high‐level feature refinement and dynamic convolution network

Published:2023-01-07 Issue: Volume: Page:
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Medical Physics

Author:

Yang Sihan¹²,Pu Qiang³,Lei Chunting¹²,Zhang Qiao⁴,Jeon Seunggil⁵,Yang Xiaomin¹²

Affiliation:

1. College of Electronics and Information Engineering Sichuan University Chengdu Sichuan China

2. School of Aeronautics and Astronautics Sichuan University Chengdu Sichuan China

3. Department of Thoracic Surgery West China Hospital Sichuan University Chengdu Sichuan China

4. Macro Net Communication Co., Ltd. Chongqing China

5. Samsung Electronics Suwon‐si Gyeonggi‐do Republic of Korea

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.16175

Reference37 articles.

1. Estimated Risks of Radiation-Induced Fatal Cancer from Pediatric CT

2. A review on CT image noise and its denoising

3. A review on deep learning approaches for low‐dose computed tomography restoration;Kulathilake KSH;Complex & Intelligent Systems,2021

4. Deep learning

5. Breast ultrasound image segmentation: A coarse‐to‐fine fusion convolutional neural network

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges;Micron;2024-05

2. Opportunities for artificial intelligence in radiation protection;Die Radiologie;2023-06-22

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