U-Net Autoencoder for Edge-Preserved Denoising of Low Dose Computed Tomography Images: A Novel Technique
Author:
Affiliation:
1. Universiti Teknologi Petronas,Department of Computer and Information Sciences,Seri Iskandar,Malaysia,32160
2. The University of Texas MD Anderson Cancer Center,Department of Imaging Physics,Houston,Texas,USA,77030
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10262399/10262497/10262803.pdf?arnumber=10262803
Reference26 articles.
1. Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising
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3. Single Low-Dose CT Image Denoising Using a Generative Adversarial Network With Modified U-Net Generator and Multi-Level Discriminator
4. Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
5. Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising
Cited by 3 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. Advancing healthcare with LDCT image denoising through self-regularization and UDA;Signal, Image and Video Processing;2024-05-08
2. Enhancing Low-Dose CT Image Quality Through Deep Learning: A DoG-Sharpened U-Net Approach With Attention Mechanism;2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS);2024-01-28
3. Enabling Predication of the Deep Learning Algorithms for Low-Dose CT Scan Image Denoising Models: A Systematic Literature Review;IEEE Access;2024
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