Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution-Reference-Cited by-同舟云学术

Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution

Published:2024-09 Issue:9 Volume:35 Page:12251-12262
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Feng Chun-Mei¹^ORCID,Yan Yunlu²,Yu Kai¹^ORCID,Xu Yong³^ORCID,Fu Huazhu¹^ORCID,Yang Jian⁴^ORCID,Shao Ling⁴^ORCID

Affiliation:

1. Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Fusionopolis Way, Singapore

2. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

3. PCA Laboratory, the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, and the Jiangsu Key Laboratory of Image and Video Understanding for Social Security, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

4. UCAS-Terminus AI Lab, University of Chinese Academy of Sciences, Beijing, China

Funder

Shenzhen Science and Technology Innovation Committee

Agency for Science, Technology and Research (A*STAR) AME Programmatic Funds

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5962385/10663876/10443261.pdf?arnumber=10443261

Reference70 articles.

1. Accelerating magnetic resonance imaging via deep learning

2. DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution

3. Specificity-Preserving Federated Learning for MR Image Reconstruction