Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages

Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data has the potential to boost the accuracy of infant age prediction thanks to the complementary information provided by different imaging modalities. However, functional connectivity m...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 39; H. 12; S. 4137 - 4149
Hauptverfasser:	Hu, Dan, Zhang, Han, Wu, Zhengwang, Wang, Fan, Wang, Li, Smith, J. Keith, Lin, Weili, Li, Gang, Shen, Dinggang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Age Algorithms autoencoder Biological system modeling Biomarkers Brain Brain mapping Brain modeling Data models Functional magnetic resonance imaging Infant age prediction Magnetic resonance imaging Medical imaging multimodal machine learning Neural networks Neuroimaging Predictions Predictive models Reconstruction Resonance
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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