Pingjun Chen-Homepage
Introduction
Dr. Chen currently serves as Principal Data Scientist at the Institute for Data Science in Oncology of
The University of Texas MD Anderson Cancer Center dedicated to Computational Pathology. He received a B.S. degree in software engineering and an M.S. degree in medical image analysis from
the Dalian University of Technology in 2012 and 2015. He received a Ph.D. degree in biomedical engineering
from the University of Florida in 2020.
He concentrates on developing cutting-edge computational models and frameworks to advance cancer interception, diagnosis, and treatment.
Three pillars of his research are 1) AI-assisted pathology analysis to automate routine pathologic diagnosis,
2) data- and biology-driven approaches to identify novel pathological biomarkers,
and 3) intelligent multimodal integration to facilitate personalized cancer therapy.
Dr. Chen aims to harmonize the perspectives of pathologists, oncologists, biologists, and computer scientists towards developing robust solutions to address critical unmet oncology needs.
Updates
Honors & Awards
- 2023, MDACC Fellowship Development Institute Grant Writing Cohort
- 2022, IEEE TMI Silver Distinguished Reviewer Award
Recent Publications
*Equal Contribution
- Pingjun Chen*, Frank Rojas*, Xin Hu*, et al.,
Pathomic Features Reveal Immune and Molecular Evolution from Lung Preneoplasia to Invasive Adenocarcinoma,
in Modern Pathology, 2023.
Presentation
- Siba El Hussein*, Pingjun Chen*, et al.,
Artificial Intelligence-Assisted Mapping of Proliferation Centers Allows the Distinction of Accelerated Phase from Large Cell Transformation in Chronic Lymphocytic Leukemia,
in Modern Pathology, 2022.
- Siba El Hussein*, Pingjun Chen*, et al.,
Artificial Intelligence Strategy Integrating Morphologic and Architectural Biomarkers Provides Robust Diagnostic Accuracy for Disease Progression in Chronic Lymphocytic Leukemia,
in The Journal of Pathology, 2021.
Invited Commentary
- Pingjun Chen*, Muhammad Aminu*, Siba El Hussein*, et al.,
Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms,
in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021.
Presentation
Patents
- Joseph D. Khoury, Jia Wu, Siba EI Hussein & Pingjun Chen,
Methods and Systems for Determining Leukemia or Lymphoma Levels using Lymphoid Images. 2023.
WO/2023/137433
- Shanhui Sun, Pingjun Chen, Xiao Chen, Zhang Chen & Terrence Chen,
Systems and Methods for Image Segmentation, 2022.
US11488021B2
- Xiao Chen, Pingjun Chen, Zhang Chen, Terrence Chen & Shanhui Sun,
Anatomy-Aware Motion Estimation, 2021.
US20210397886A1
Software
- tissueloc:
Whole Slide Digital Pathology Image Tissue Localization.
[docs]
- pyslide:
Whole Slide Digital Pathology Image Analysis Toolbox.
[docs]
Datasets
- Pingjun Chen, Frank Rojas, et al.,
Lung Adenocarcinoma Evolution H&E Pathomic Feature Analysis Dataset, 2023.
Mendeley Data
- Pingjun Chen,
Knee Osteoarthritis Severity Grading Dataset, 2018.
Mendeley Data