Han Zhang

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Assistant Professor

University of North Carolina at Chapel Hill

United States

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Han Zhang is an Assistant Professor at the University of North Carolina at Chapel Hill, USA. His research areas include deep learning applications in neuroimaging, brain connectivity, and the development of infant brain function. His recent publications emphasize longitudinal brain MRI synthesis, memory-based semantic alignment in radiology, and individualized assessments of brain conditions using advanced imaging techniques.

Recent Grants

Grant: Close

Quantifying Brain Abnormality by Multimodality Neuroimage Analysis

Open Date: 2018-10-01

Close Date: 2020-03-01

Grant: Close

Parcellating Infant Cerebral Cortex based on Developmental Patterns of Multimodal MRI

Open Date: 2018-07-01

Close Date: 2023-06-01

Grant: Close

Diagnosis of Alzheimer's Disease Using Dynamic High-Order Brain Networks

Open Date: 2016-09-01

Close Date: 2019-09-01

Grant: Close

UNC/UMN Baby Connectome Project

Open Date: 2016-06-01

Close Date: 2020-05-01

Grant: Close

Multimodal imaging based surgical mapping

Open Date: 2014-01-01

Close Date: 2017-12-01

Articles (16)

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2020

Collaborators (4)

Gang Li

Assistant Professor

University of North Carolina at Chapel Hill

UNITED STATES

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Adeel Razi

University College London

UNITED KINGDOM

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kaicong sun

University of Stuttgart

GERMANY

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J Keith Smith

Professor of Neuroradiology

University of North Carolina at Chapel Hill School of Medicine

UNITED STATES

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Han Zhang

Research Interests

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About

Recent Grants

Articles (16)

A Deep Learning Framework for Synthesizing Longitudinal Infant Brain MRI during Early Development

SRM-Net: Joint Sampling and Reconstruction and Mapping Network for Accelerated 3T Brain Multi-Parametric MR Imaging

Evaluating the evolution and inter-individual variability of infant functional module development from 0 to 5 yr old

Memory-Based Cross-Modal Semantic Alignment Network for Radiology Report Generation

A Unified Multi-Modality Fusion Framework for Deep Spatio-Spectral-Temporal Feature Learning in Resting-State fMRI Denoising

Outcome Prediction of Unconscious Patients Based on Weighted Sparse Brain Network Construction

Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity

Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning

Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction

Brain Metabolic Network Redistribution in Patients with White Matter Hyperintensities on MRI Analyzed with an Individualized Index Derived from 18F-FDG-PET/MRI

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

Development of Dynamic Functional Architecture during Early Infancy

The emergence of a functionally flexible brain during early infancy

A toolbox for brain network construction and classification (BrainNetClass)

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Large‐scale dynamic causal modeling of major depressive disorder based on resting‐state functional magnetic resonance imaging

Collaborators (4)

Gang Li

Adeel Razi

kaicong sun

J Keith Smith

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