His research concentrates on developing cutting-edge computational tools and frameworks to advance cancer prevention, detection, diagnosis, and treatment.
Three pillars of his research are 1) AI-assisted digital pathology image analysis to automate routine pathologic diagnosis and prognosis,
2) data- and biology-driven approaches to identify novel biomarkers,
and 3) intelligent multi-modality data integration to facilitate personalized cancer therapy.
Dr. Chen aims to harmonize the perspectives of pathologists, oncologists, biologists, and computer scientists toward developing robust solutions to address critical unmet needs for making cancer history.
[2023/08/31] One first-authored journal paper was accepted by Modern Pathology (IF: 7.5).
2023, MDACC Fellowship Development Institute Grant Writing Cohort
2022, IEEE TMI Silver Distinguished Reviewer Award
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.
Pingjun Chen*, Siba El Hussein*, et al.,
Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering,
in Cancers, 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.