Federated Domain Adaptation via Transformer for Multi-Site Alzheimer’s Disease Diagnosis-Reference-Cited by-同舟云学术

Federated Domain Adaptation via Transformer for Multi-Site Alzheimer’s Disease Diagnosis

Published:2023-12 Issue:12 Volume:42 Page:3651-3664
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Lei Baiying¹^ORCID,Zhu Yun¹^ORCID,Liang Enmin¹,Yang Peng¹^ORCID,Chen Shaobin²^ORCID,Hu Huoyou³,Xie Haoran⁴^ORCID,Wei Ziyi⁵,Hao Fei⁵,Song Xuegang¹^ORCID,Wang Tianfu¹^ORCID,Xiao Xiaohua³,Wang Shuqiang⁶^ORCID,Han Hongbin⁵^ORCID

Affiliation:

1. School of Biomedical Engineering, Shenzhen University Medical School, Marshall Laboratory of Biomedical Engineering, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China

2. Biomedical Engineering, Shenzhen University, Shenzhen, China

3. Department of Geriatrics, The First Affiliated Hospital of Shenzhen University, Shenzhen University Medical School, Shenzhen Second People’s Hospital, Shenzhen University, Shenzhen, China

4. Department of Computing and Decision Sciences, Lingnan University, Tuen Mun, Hong Kong

5. Department of Radiology, Peking University Third Hospital, Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Institute of Medical Technology, Peking University Health Science Center, Beijing, China

6. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10336247/10198494.pdf?arnumber=10198494

Reference68 articles.

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5. Predicting clinical scores for Alzheimer’s disease based on joint and deep learning

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