Area of research
Artificial Intelligence · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include AI in cancer detection, Radiomics and Machine Learning in Medical Imaging, Cell Image Analysis Techniques, and Glioma Diagnosis and Treatment.
Landscape of subclinical rejection in a large international cohort of pediatric kidney transplant recipients
Morphological set enrichment enables interpretable prognostication and molecular profiling of meningiomas
HistomicsTK: A Python toolkit for pathology image analysis algorithms
Benchmarking pathology foundation models for non-neoplastic pathology in the placenta
An Accelerated Spectroscopic MRI Metabolite Quantification Based on a Deep Learning Method for Radiation Therapy Planning in Brain Tumor Patients.
Informatics at the Frontier of Cancer Research.
A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer.
Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer
A panoptic segmentation dataset and deep-learning approach for explainable scoring of tumor-infiltrating lymphocytes.
A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer
Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry
An Automated Pipeline for Differential Cell Counts on Whole-Slide Bone Marrow Aspirate Smears
A population-level computational histologic signature for invasive breast cancer prognosis
Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students
Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry
Video 1 from Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers
Supplementary Tables from Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers
Tissue contamination challenges the credibility of machine learning models in real world digital pathology
Video 1 from Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers
Data from Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers
Supplementary Data from Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers
Data from Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers
Supplementary Data from Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers
Supplementary Tables from Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers
Glioma progression is shaped by genetic evolution and microenvironment interactions
NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer
Collaborative Research: Taking the Pulse of the Arctic Ocean - A US Contribution to the International Synoptic Arctic Survey
Acquisition of Stable Isotope Mass Spectrometry Instrumentation for High Latitude and Marine Applications at the University of Maryland Center for Environmental Science
Science Plan and Workshop Support for Bering Strait Environmental Observations
Science Management for the Raise and Land-Shelf Interactions (LSI) Science Project Office
Long-Term Observations: An Arctic Environmental Observatory in Bering Strait
Chemical and Isotopic Tracers on the U.S/Canada Arctic Ocean Section