Gang Qu

Orcid: 0000-0003-2681-0880

Affiliations:
  • Tulane University, Department of Biomedical Engineering, New Orleans, LA, USA


According to our database1, Gang Qu authored at least 19 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A Deep Dynamic Causal Learning Model to Study Changes in Dynamic Effective Connectivity During Brain Development.
IEEE Trans. Biomed. Eng., December, 2024

Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks.
IEEE Trans. Medical Imaging, April, 2024

Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development.
NeuroImage, 2024

Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis.
Medical Image Anal., 2024

Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks.
CoRR, 2024

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.
CoRR, 2024

An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis.
CoRR, 2024

Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies.
CoRR, 2024

2023
Latent Similarity Identifies Important Functional Connections for Phenotype Prediction.
IEEE Trans. Biomed. Eng., June, 2023

Dynamic weighted hypergraph convolutional network for brain functional connectome analysis.
Medical Image Anal., 2023

2022
Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies.
IEEE Trans. Biomed. Eng., 2022

Brain Functional Connectivity Analysis via Graphical Deep Learning.
IEEE Trans. Biomed. Eng., 2022

Deep Learning in Neuroimaging: Promises and challenges.
IEEE Signal Process. Mag., 2022

Phenotype guided interpretable graph convolutional network analysis of fMRI data reveals changing brain connectivity during adolescence.
Proceedings of the Medical Imaging 2022: Biomedical Applications in Molecular, 2022

2021
Interpretable Multimodal Fusion Networks Reveal Mechanisms of Brain Cognition.
IEEE Trans. Medical Imaging, 2021

Ensemble Manifold Regularized Multi-Modal Graph Convolutional Network for Cognitive Ability Prediction.
IEEE Trans. Biomed. Eng., 2021

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction.
CoRR, 2021

2020
Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis.
Medical Image Anal., 2020

A graph deep learning model for the classification of groups with different IQ using resting state fMRI.
Proceedings of the Medical Imaging 2020: Biomedical Applications in Molecular, 2020


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