Xiang Li

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Assistant Professor

Harvard Medical School

United States

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About

Dr. Xiang Li is an Assistant Professor at Harvard Medical School in the United States. His research focuses on biomedicine, particularly in the areas of artificial intelligence applications in medical imaging, text data augmentation, and predictive modeling for neurological diseases. Recent publications include work on leveraging ChatGPT for data enhancement and a generalist vision-language model for biomedical tasks, showcasing his contributions to advancing the capabilities of medical technology.

Recent Grants

Grant: Close

Identification of Multi-modal Imaging Biomarkers for Early Prediction of MCI-AD Conversion via Multigraph Representation

Open Date: 2022-08-01

Close Date: 2024-07-31

Grant: Close

Deep learning Based Phenotyping and Treatment Optimization for Heart Failure with Preserved Ejection Fraction

Open Date: 2022-03-15

Close Date: 2026-02-28

Grant: Close

Study of Brain Spreading Pathways of Tau and Amyloid-Beta in (pre)clinical AD

Open Date: 2017-08-15

Close Date: 2021-06-30

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2020

Collaborators (7)

Linus Manubens-Gil

Assistant Professor

Universitat Politècnica de Catalunya

SPAIN

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Dufan Wu

Associate Professor

Ohio State University

UNITED STATES

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Filipe Moura

Assistant Professor

Yale University

UNITED STATES

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Quanzheng Li

University of Georgia

UNITED STATES

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Alessandro Bria

ITALY

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Wei Liu

Professor

Mayo Clinic Arizona

UNITED STATES

View details

Zhengliang Liu

University of Georgia

UNITED STATES

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Xiang Li

Research Interests

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About

Recent Grants

Articles (27)

AugGPT: Leveraging ChatGPT for Text Data Augmentation

Artificial General Intelligence for Medical Imaging Analysis

Temporal-Spatial Adaptation of Promptable SAM Enhance Accuracy and Generalizability of Cine CMR Segmentation

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

An Iterative Optimizing Framework for Radiology Report Summarization With ChatGPT

CE-GAN: Community Evolutionary Generative Adversarial Network for Alzheimer’s Disease Risk Prediction

Instruction-ViT: Multi-modal prompts for instruction learning in vision transformer

Tailoring Large Language Models to Radiology: A Preliminary Approach to LLM Adaptation for a Highly Specialized Domain

Assessment of valve regurgitation severity via contrastive learning and multi-view video integration

A generalist vision-language foundation model for diverse biomedical tasks

A generalist vision–language foundation model for diverse biomedical tasks

Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction

ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs

Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training

Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study

Fine-Grained Image-Text Alignment in Medical Imaging Enables Cyclic Image-Report Generation

Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution

ClinicalRadioBERT: Knowledge-Infused Few Shot Learning for Clinical Notes Named Entity Recognition

Editorial: Multi-dimensional characterization of neuropsychiatric disorders

Federated learning for predicting clinical outcomes in patients with COVID-19

Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-Ventricular Short-Axis Cardiac MR Data

Characterization of Brain Iron Deposition Pattern and Its Association With Genetic Risk Factor in Alzheimer’s Disease Using Susceptibility-Weighted Imaging

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19

Assessing Fine-Granularity Structural and Functional Connectivity in Children With Attention Deficit Hyperactivity Disorder

Four-Dimensional Modeling of fMRI Data via Spatio–Temporal Convolutional Neural Networks (ST-CNNs)

Automated Semantic Segmentation of Red Blood Cells for Sickle Cell Disease

Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A Task fMRI Study

Collaborators (7)

Linus Manubens-Gil

Dufan Wu

Filipe Moura

Quanzheng Li

Alessandro Bria

Wei Liu

Zhengliang Liu

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