Lei Wu
Orcid: 0000-0002-9705-1829Affiliations:
- Georgia Institute of Technology, Emory University, Georgia State University, Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA
- University of New Mexico, Department of ECE, Mind Research Network, Albuquerque, NM, USA (PhD 2015)
According to our database1,
Lei Wu
authored at least 25 papers
between 2008 and 2025.
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Bibliography
2025
Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis.
CoRR, May, 2025
Dynamic Fusion: Merging Structural and Functional Connectivity Dynamics Via Joint CMICA.
Proceedings of the 22nd IEEE International Symposium on Biomedical Imaging, 2025
Genetics Encoded Joint Embedding of Multimodal Connectomes with Explainable Graph Neural Network for Schizophrenia Classification.
Proceedings of the 22nd IEEE International Symposium on Biomedical Imaging, 2025
2024
Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links.
NeuroImage, January, 2024
Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.
NeuroImage, 2024
Physics-Guided Multi-view Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling.
Proceedings of the Predictive Intelligence in Medicine - 7th International Workshop, 2024
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
2023
Joint Structural and Functional Connectivity Learning Based Independent Component Analysis.
Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023
2022
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes.
CoRR, 2022
2021
Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data.
CoRR, 2021
On Self-Supervised Multimodal Representation Learning: An Application To Alzheimer's Disease.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021
Proceedings of the 9th IEEE International Conference on Healthcare Informatics, 2021
2020
On self-supervised multi-modal representation learning: An application to Alzheimer's disease.
CoRR, 2020
2018
An approach to directly link ICA and seed-based functional connectivity: Application to schizophrenia.
NeuroImage, 2018
2015
Independent Vector Analysis for Gradient Artifact Removal in Concurrent EEG-fMRI Data.
IEEE Trans. Biomed. Eng., 2015
Identifying brain dynamic network states via GIG-ICA: Application to schizophrenia, bipolar and schizoaffective disorders.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015
2014
Gradient artifact removal in concurrently acquired EEG data using independent vector analysis.
Proceedings of the IEEE International Conference on Acoustics, 2014
2013
The spatiospectral characterization of brain networks: Fusing concurrent EEG spectra and fMRI maps.
NeuroImage, 2013
Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013
2011
Parallel independent component analysis using an optimized neurovascular coupling for concurrent EEG-fMRI sources.
Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
2010
Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: A concurrent EEG-fMRI study.
NeuroImage, 2010
2008
Proceedings of the IEEE International Conference on Acoustics, 2008