Gang Li

Assistant Professor

University of North Carolina at Chapel Hill

United States

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Dr. Gang Li is an Assistant Professor at the University of North Carolina at Chapel Hill, USA. His research focuses on advanced imaging techniques and machine learning applications in the field of medical diagnostics, with particular emphasis on radiology and neuroimaging. Recent publications include studies on large language models for radiology tasks, skull-stripping models for MRI, and segmentation methodologies for infant and Alzheimer's disease analysis.

Articles (16)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2021

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2020

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2020

Collaborators (11)

Han Zhang

Assistant Professor

University of North Carolina at Chapel Hill

UNITED STATES

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Zhengwang Wu

University of North Carolina at Chapel Hill

UNITED STATES

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Li Wang

Associate Professor

University of North Carolina at Chapel Hill

UNITED STATES

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Jakob Seidlitz

University of Pennsylvania

UNITED STATES

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Kathryn Humphreys

Assistant Professor

Vanderbilt University

UNITED STATES

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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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Dinggang Shen

University of North Carolina at Chapel Hill

UNITED STATES

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Hao Ni

Professor

University College London

UNITED KINGDOM

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Mingxia Liu

University of North Carolina at Chapel Hill

UNITED STATES

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Pew-Thian Yap

Adjunct Professor of Computer Science

University of North Carolina at Chapel Hill

UNITED STATES

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Zhengliang Liu

University of Georgia

UNITED STATES

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Gang Li

Research Interests

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About

Articles (16)

A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases

Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

Flexible Individualized Developmental Prediction of Infant Cortical Surface Maps via Intensive Triplet Autoencoder

Incomplete Multi-modal Disentanglement Learning with Application to Alzheimer’s Disease Diagnosis

PETS-Nets: Joint Pose Estimation and Tissue Segmentation of Fetal Brains Using Anatomy-Guided Networks

A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks

Multi-Template Meta-Information Regularized Network for Alzheimer’s Disease Diagnosis Using Structural MRI

Multifaceted atlases of the human brain in its infancy

Self-supervised learning with application for infant cerebellum segmentation and analysis

Fine-grained functional parcellation maps of the infant cerebral cortex

iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction

Path Signature Neural Network of Cortical Features for Prediction of Infant Cognitive Scores

Recurrent Tissue-Aware Network for Deformable Registration of Infant Brain MR Images

S3Reg: Superfast Spherical Surface Registration Based on Deep Learning

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

Hierarchical Rough-to-Fine Model for Infant Age Prediction Based on Cortical Features

Collaborators (11)

Han Zhang

Zhengwang Wu

Li Wang

Jakob Seidlitz

Kathryn Humphreys

J Keith Smith

Dinggang Shen

Hao Ni

Mingxia Liu

Pew-Thian Yap

Zhengliang Liu

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