Multi-Scale Dilation with Residual Fused Attention Network for Low Dose CT Noise Artifact Reductions-Reference-Cited by-同舟云学术

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Multi-Scale Dilation with Residual Fused Attention Network for Low Dose CT Noise Artifact Reductions

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
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Author:

Freire Kevin¹,Alirezaie Javad¹,Babyn Paul²

Affiliation:

1. Toronto Metropolitan University,Department of Electrical & Computer Engineering,Toronto,Canada

2. University of Saskatoon Health Region,Department of Medical Imaging,Saskatoon,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230523.pdf?arnumber=10230523

Reference12 articles.

1. Deep residual learning for image recognition;he,2015

2. Low Dose CT Noise Artifact Reduction Based on Multi-scale Weighted Convolutional Coding Network

3. Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer

4. Gradient-based Optimization Algorithm for Hybrid Loss Function in Low-dose CT Denoising

5. Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising

Cited by 1 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. A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective;Biomedical Engineering Letters;2024-08-30

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