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Junhan Zhao

Assistant Professor

University of Chicago

United States

Research Interests

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Professors in United States Professors in Medical Imaging Medical Imaging professors in United States

About

Junhan Zhao is an Assistant Professor at the University of Chicago, USA. His research interests encompass computational pathology, deep learning applications in medical imaging, and the association between dietary factors and cancer risk. Recent publications include studies on active learning in computational pathology, uncertainty-aware models for glioblastoma differentiation, and the development of digital biomarkers for cognitive assessment in older adults.

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Collaborators (11)

Dongfang Liu

Associate Professor

Purdue University

UNITED STATES

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Xiao Han

Professor

Sichuan University

CHINA

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Ting-Wan Kao

Harvard University

UNITED STATES

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Yuxin Yang

Research Data Scientist

Cleveland Clinic

UNITED STATES

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Jieqiong Zhao

Assistant Professor

Augusta University

UNITED STATES

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Thomas Roetzer-Pejrimovsky

Medical University of Vienna

AUSTRIA

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Feixiong Cheng

Faculty Member

Case Western Reserve University

UNITED STATES

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Jeffrey Golden

Executive Vice Dean, Research and Education

Cedars-Sinai Medical Center

UNITED STATES

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Dan Lin

Cornell University

UNITED STATES

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Kangning Zhang

Professor

Beihang University

CHINA

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盛屹刘

Sichuan University

CHINA

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Junhan Zhao

Research Interests

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About

Articles (24)

A Hybrid Quantum–AI Framework for Protein Structure Prediction on NISQ Devices

A Generalizable Multimodal Model for Treatment‐Stratified Risk and Survival Assessment under Real‐World Constraints: A Multi‐Center Study of Colorectal Cancer

Digital Phenotyping of Physical Activity and Mortality in Cancer Survivors

Benchmarking Open-Source Pathology Foundation Models for Breast Cancer Biomarker Prediction from H&E Whole-Slide Images

A unified vision-language model for precision oncology and biomarker prediction in neuroblastoma

A Generative Foundation Model for Chest Radiography

AI-powered medicinal chemistry and translational drug development

Active learning in computational pathology with noise detection empowered by loss-based prior and feature analysis

Uncertainty-aware ensemble of foundation models differentiates glioblastoma from its mimics

Pancancer outcome prediction via a unified weakly supervised deep learning model

Association between ultra-processed food consumption and lung cancer risk: a population-based cohort study.

Accelerometer-Measured Multi-dimensional Physical Activity Patterns and Physical Functional Decline: A Prospective National Cohort Study of Aging.

Advancing hierarchical neural networks with scale-aware pyramidal feature learning for medical image dense prediction

Predicting the Distribution of Ailanthus altissima Using Deep Learning-Based Analysis of Satellite Imagery

From physical activity patterns to cognitive status: development and validation of novel digital biomarkers for cognitive assessment in older adults.

Association of whole grain food consumption with lung cancer risk: a prospective cohort study

Deep Learning-Enabled Integration of Histology and Transcriptomics for Tissue Spatial Profile Analysis

Towards General-Purpose Video Reconstruction through Synergy of Grid-Splicing Diffusion and Large Language Models

Contrastive learning enhances fairness in pathology artificial intelligence systems.

AI‐Driven Acceleration of Fluorescence Probe Discovery

Site-specific pain dynamics: associations between accelerometer-measured physical activity patterns and pain in older adults

Beyond the Clock: Exploring Multimodal Behavior Markers of Mild Cognitive Impairment in Older Adults during Clock Drawing Test

Physical activity levels may impact on the risk of premature mortality in people with epilepsy

Prompt-based multimodal representation learning for drug repurposing

Collaborators (11)

Dongfang Liu

Xiao Han

Ting-Wan Kao

Yuxin Yang

Jieqiong Zhao

Thomas Roetzer-Pejrimovsky

Feixiong Cheng

Jeffrey Golden

Dan Lin

Kangning Zhang

盛屹 刘

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盛屹刘