Quantitative BioImaging Lab.
"seeing more with deep learning"
Biography: Dr. Young Hwan Chang received his Ph.D in Mechanical Engineering from University of California, Berkeley under the supervision of Prof. Claire J. Tomlin (EECS). Then, he worked as a postdoctoral researcher at Hybrid Systems Laboratory in the Department of Electrical Engineering and Computer Sciences, UC Berkeley. Previously, he also worked at Cognitive Robotics Research Center, Korea Institute of Science and Technology (KIST) and Hyundai-Kia R&D Center.
Research Interests: Quantitative imaging analysis, Data-driven system identification, Control theory, Modeling of biological systems, Estimation, Brain-Machine-Interface, Machine Learning
Lab Philosophy: Passionate & Self-motivated Team Player
".. Proving that something can be done that many people said was impossible. - Elon Musk"
What's up in QBI Lab?
Aug, 2020: Congratulations to Geoff on his successful virtual defense !! The first Chang Lab PhD! He will join Digital Pathology & AI oncology Biomarker Discovery Group @ Genentech this Fall.
July, 2020: Congrats Geoff, Erik and Elliot! three abstracts (one oral and two poster presentations) were selected for 2020 CSBC/PS-ON/BD-STEP Junior Investigator Annual Meeting.
Elliot Gray, "Elucidating intratumoral T and B cell functionality related to spatial metrics for pancreatic cancer patient stratification via interpretable machine learning” [Oral]
Erik Burlingame, "Comprehensive profiling of breast cancer cell state via multiplex imaging reveals subtype-specific tumor cell differentiation and spatial distribution” [Poster]
Geoff Schau, "Generalistic AI framework for analysis of intracellular staining patterns reveals checkpoint marker activity in immune tissues compromised by metastasis” [Poster]
July, 2020: Congrats Tina. She got an invitation to attend Multi-Scale Modeling Summer School & Hackathon
July, 2020: Erik presented "Tools for large-scale single-cell imaging projects" @ CSBC/PS-ON Resource and Data Sharing WG Meeting
July, 2020: Congratulations Tina for passing her PhD qualifying exam!
July, 2020: Congrats Geoff & Erik! Two papers have been accepted for presentation at, and publication in the proceedings of, the 2020 59th IEEE Conference on Decision and Control (CDC), to be held in a fully virtual configuration during December 2020.
Geoff Schau, Erik Burlingame, Young Hwan Chang, "DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping"
Young Hwan Chang, Jeremy Linsley, Josh Lamstein, Jaslin Kalra, Irina Epstein, Mariya Barch, Kenneth Daily, Phil Synder, Laura Heiser, Steve Finkbeiner, "Single cell tracking based on Voronoi partition via stable matching"
June, 2020: Congrats Elliot and Erik! Poster session award (2nd prize) at OHSU-PSU Machine Learning for Health Workshop
June, 2020: Congrats Elizabeth and Pepper!!. "Loss of myoepithelial calponin‐1 characterizes high risk ductal carcinoma in situ cases" is selected for the cover of Molecular Carcinogenesis (https://onlinelibrary.wiley.com/toc/10982744/2020/59/7)
June, 2020: Congrats Elliot, Erik, Geoff, Luke! 4 abstracts have been accepted for a short presentation on OHSU - PSU Machine Learning for Health Workshop (http://www.pi4cs.org/mlhworkshop)
Elliot Gray, Shannon Liudahl, Shamilene Sivagnanam, Courtney Betts, Jason Link, Dove Keith, Brett Sheppard, Rosalie Sears, Guillaume Thibault, Joe W. Gray, Lisa M. Coussens, Young Hwan Chang, "Elucidating intratumoral T and B cell functionality related to spatial metrics for pancreatic cancer patient stratification via interpretable machine learning"
Erik Burlingame, Jennifer Eng, Guillaume Thibault, Geoffrey Schau, Koei Chin, Joe W. Gray, Young Hwan Chang, "Balanced learning of cell state representations"
Geoffrey Schau, Hassan Ghani, Erik Burlingame, Joe Gray, Chris Corless, Young Hwan Chang, "Unsupervised Histological Feature Manifold Learning for Massively Parallel Whole Slide Annotation"
Luke Ternes, Ge Huang, Christian Lanciault, Guillaume Thibault, Rachelle Rigger, Joe W. Gray, John Muschler, Young Hwan Chang, "VISTA: Virtual ImmunoSTAining for pancreatic disease quantification in murine cohorts",
June, 2020: Congrats Rajan! Our collaborative application entitled "Detection of Early Cancer with Deep Histopathological Analysis" will be supported by International Alliance for Cancer Early Detection (ACED) (PIs: Kulkani and Marais) - Looking forward to working on this project!!
April, 2020: YH is an International Alliance for Cancer Early Detection (ACED) alliance member from OHSU (as of April 15, 2020)
April, 2020: YH has been accepted to attend the 2020 Advancing Cancer Biology at the Frontiers of Machine Learning and Mechanistic Modeling Innovation Lab (On behalf of the National Cancer Institute and Carnegie Mellon University) (link)
April, 2020: New bioRxiv paper: Ternes et al, VISTA: Virtual ImmunoSTAining for pancreatic disease quantification in murine cohorts
Mar, 2020: Our new paper is published in Communications Biology, "Restore: Robust intEnSiTy nORmalization mEthod for multiplexed imaging" (link)
Mar, 2020: Geoff, Erik, Luke, Elliot attended CSBC/PS-ON Image Analysis Hackathon, Vanderbilt University
Mar, 2020: Congratulations Geoff, Erik and Luke !!
Geoff & Erik for the award of a fellowship for the Giersch Conference & Summer School in Frankfurt am Main (https://fias.institute/en/events/all-events/theoretical-and-experimental-quantitative-cell-biology/)
Luke for the travel award of up to $2,500 to present at the JCA-AACR conference (https://www.aacr.org/meeting/jca-aacr-pancreatic-cancer/)
Feb, 2020: Congratulations Luke! "Iterative Deep Learning Based Segmentation on Cyclic Immunofluorescence Imaging by using recursive refinement" has been accepted for presentation at the IEEE International Symposium on Biomedical Imaging (ISBI'20), the Marriott Coralville, Iowa City, April 3 - 7, 2020.
Feb, 2020: New CEDAR funded project "Developing 3D bioprinted tissue models to interrogate epithelial-stromal crosstalk in early breast cancer (PI: Langer)"
Feb, 2020: Our paper "RESTORE: Robust intEnSiTy nORmalization mEthod for Multiplexed Imaging" has been accepted for publication in Communications Biology (a new life-science journal from Nature)
Feb, 2020: Collaborative paper with Schedin Lab, "Loss of myoepithelial calponin-1 characterizes high risk ductal carcinoma in situ cases, which are further stratified by T cell composition" has been accepted for publication in Molecular Carcinogenesis, doi:10.1002/mc.23171
Feb, 2020: Our collaborative application to the Early Detection - CRUK-OHSU Project Award was selected for funding (PIs: Drs. Zhuang and Chang) (£250,000 for CRUK- & $300,000 for OHSU-based researchers)
Feb, 2020: YH attended OHSU Invent-a-thon Problem Statement Workshop (https://inventathon.org/)
Feb, 2020: YH gave a talk "RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging" at the Knight Research Lecture Series.
Feb, 2020: Congrats Geoff! “Predicting Primary Site of Secondary Liver Cancer with a Neural Estimator of Metastatic Origin (NEMO)” has been accepted for publication in the Journal of Medical Imaging (Evaluation Methodologies for Clinical AI Special Section)
Geoffrey F. Schau, Erik A. Burlingame, Guillaume Thibault, Tauangtham Anekpuritanang, Ying Wang, Joe W. Gray,Christopher Corless, Young Hwan Chang, "Predicting primary site of secondary liver cancer with a neural estimator of metastatic origin," J. Med. Imag. 7(1), 012706 (2020), doi: 10.1117/1.JMI.7.1.012706.
Feb, 2020: YH will serve on the program committee (PC) of ISMCO'20 (International Symposium on Mathematical and Computational Oncology).
Jan, 2020: Our collaborative application to the Knight Pilot Award was selected for funding (PIs: Drs. Vu, Lind, Chang), "Imaged-based Machine Learning For Comprehensive Identification of Drug Combination Effectiveness on Immune Cell-Mediated Tumor Cell Kill"
Jan, 2020: Luke, Geoff, Jenny and Erik attended Cancer Systems Biology Consortium (CSBC)/Physical Sciences-Oncology Network (PS-ON) Image Analysis Workshop
Jenny Eng, "The intensity normalization problem in multiplex imaging"
Geoff Schau, "Unsupervised morphology learning for single-cell sub-population detection"
Luke Ternes, "Recursive Segmentation Refinement Without Manual Annotations"