Young Hwan Chang

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Associate Professor

Oregon Health & Science University

United States

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About

Young Hwan Chang is an Associate Professor at Oregon Health and Science University in the United States. His research focuses on digital image analysis in cancer studies, with recent articles addressing topics such as circulating neoplastic-immune hybrid cells, image integration techniques, and quantitative analysis methodologies. His work aims to enhance understanding of cancer metastasis and improve prognostic biomarker identification through advanced imaging technologies.

Recent Grants

Grant: Close

Integrative single cell analyses of inflammation-derived circulating hybrid cells to identify aggressive disease

Open Date: 2021-04-01

Close Date: 2026-03-31

Grant: Close

Omic and Multidimensional Spatial Atlas of Metastatic Breast and Prostate Cancers

Open Date: 2018-09-19

Close Date: 2023-08-31

Grant: Close

Deciphering the immune microenvironment in oesophageal adenocarcinoma and pre-cancerous lesions to a single cell resolution

Open Date: 2018-09-01

Close Date: 2019-08-01

Grant: Close

TIME matters in pancreatic cancer (tumor immune microenvironment)

Open Date: 2018-07-01

Close Date: 2019-06-01

Grant: Close

Ultra-High Content Analysis (UHCA) of Single Cells in Tissue: 60+ channel immunofluorescence labeling kits and companion imaging software for everyone

Open Date: 2017-09-21

Close Date: 2018-08-31

Articles (17)

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2025

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2022

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2022

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2022

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2021

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2021

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2020

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2020

Collaborators (15)

Gordon Mills

Oregon Health & Science University

UNITED STATES

Young Hwan Chang

Research Interests

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About

Recent Grants

Articles (17)

Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon

COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation

Exploratory Analyses of Circulating Neoplastic-Immune Hybrid Cells as Prognostic Biomarkers in Advanced Intrahepatic Cholangiocarcinoma

Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human‐level accuracy

Suppression of dystroglycan function accompanies pancreatic acinar‐to‐ductal metaplasia and favours dysplasia development

Circulating Neoplastic-Immune Hybrid Cells Are Biomarkers of Occult Metastasis and Treatment Response in Pancreatic Cancer

Morphodynamical cell state description via live-cell imaging trajectory embedding

A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis

Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model

A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice

Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays

Circulating Neoplastic-Immune Hybrid Cells Predict Metastatic Progression in Uveal Melanoma

A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration

Genomic Alterations during the In Situ to Invasive Ductal Breast Carcinoma Transition Shaped by the Immune System

SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning

RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging

Collaborators (15)

Gordon Mills

Navid Mohammad Mirzaei

Marilyne Labrie

Rosalie C. Sears

Gregory J. Baker

Leili Shahriyari

Kevin Sidak

Daniel Zuckerman

Ioannis Zervantonakis

C. Marcelo Aldaz

Melissa Wong

Colleen Cebulla

John Muschler

Skye C. Mayo

Guillaume THIBAULT

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