Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation

Accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective treatment and prognosis. Functional brain networks (FBNs) constructed from functional magnetic resonance imaging (fMRI) have become a popular tool for ASD diagnosis. However, existing model-driven approaches used to constr...

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Veröffentlicht in:	Medical & biological engineering & computing Jg. 61; H. 11; S. 2829 - 2842
Hauptverfasser:	Lei, Dajiang, Zhang, Tao, Wu, Yue, Li, Weisheng, Li, Xinwei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023 Springer Nature B.V
Schlagworte:	Algorithms Autism Autism Spectrum Disorder - diagnostic imaging Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Brain Brain - diagnostic imaging Brain mapping Classification Classifiers Computer Applications Constraint modelling Construction data collection Deep learning Diagnosis Functional magnetic resonance imaging Human Physiology Humans Image processing Imaging Machine learning Magnetic Resonance Imaging magnetism Medical diagnosis model validation Networks Neuroimaging Original Article patients prognosis Prototypes Radiology Representations Time Factors time series analysis Deep unrolling Functional brain networks Autism spectrum disorder Diagnosis-oriented Classification
ISSN:	0140-0118, 1741-0444, 1741-0444
Online-Zugang:	Volltext
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