Learning to Distill Global Representation for Sparse-View CT-Reference-Cited by-同舟云学术

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Learning to Distill Global Representation for Sparse-View CT

Published:2023-10-01 Issue: Volume:33 Page:21139-21150
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Li Zilong¹,Ma Chenglong²,Chen Jie¹,Zhang Junping¹,Shan Hongming²

Affiliation:

1. Fudan University,Shanghai Key Lab of Intelligent Information Processing, School of Computer Science,Shanghai,China,200433

2. Fudan University,Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science,Shanghai,China,200433

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10376896.pdf?arnumber=10376896

Reference60 articles.

1. Sparse-View CT Reconstruction Based on a Hybrid Domain Model with Multi-Level Wavelet Transform

2. Frequency Domain Image Translation: More Photo-realistic, Better Identity-preserving

3. Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets

4. Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

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. GREAT- IQA: Integrating Global Perception and Local Task-Specific Information for CT Image Quality Assessment;2023 IEEE International Conference on Medical Artificial Intelligence (MedAI);2023-11-18

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