A Causality-Aware Graph Convolutional Network Framework for Rigidity Assessment in Parkinsonians-Reference-Cited by-同舟云学术

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A Causality-Aware Graph Convolutional Network Framework for Rigidity Assessment in Parkinsonians

Published:2024-01 Issue:1 Volume:43 Page:229-240
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Tang Xinlu¹^ORCID,Zhang Chencheng²^ORCID,Guo Rui¹^ORCID,Yang Xinling³,Qian Xiaohua¹^ORCID

Affiliation:

1. Medical Image and Health Informatics Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

2. Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

3. Second Affiliated Hospital, Xinjiang Medical University, Ürümqi, China

Funder

Special Fund for Transformation of Scientific and Technological Achievements of Inner Mongolia Autonomous Region of China

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Med-Engineering Crossing Foundation from Shanghai Jiao Tong University

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/ielx7/42/10379496/10177980.pdf?arnumber=10177980

Reference52 articles.

1. Parkinson's disease: clinical features and diagnosis

2. The burden of Parkinson's disease: a worldwide perspective

3. Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results

4. New methods for the assessment of Parkinson's disease (2005 to 2015): A systematic review

5. Progression of motor symptoms in Parkinson’s disease

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1. A Novel Dual Interactive Network for Parkinson’s Disease Diagnosis Based on Multi-modality Magnetic Resonance Imaging;Lecture Notes in Computer Science;2024

2. MADP: Multi-modal Sequence Learning for Alzheimer’s Disease Prediction with Missing Data;Communications in Computer and Information Science;2024

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