The Imaging Informatics research area focuses on extracting knowledge from medical imaging data. Medical imaging data contains vast amounts of anatomical and physiological information to classify and understand the progression of disease. Our group specializes in combining the deep insights gleaned from medical imaging data with the medical record through standards-based data models to enable multimodal research.
Our team includes imaging scientists who focus on deep learning methods for medical image segmentation, clinical imaging specialists in Radiology and Ophthalmology, and informaticists. Our group includes Paul Nagy, PhD, a fellow of the Society of Imaging Informatics in Medicine (SIIM), past society chair, and past chair of the college of SIIM fellows; and Teri Sippel Schmidt, MS, a fellow of SIIM and a recipient of the SIIM gold medal.
Our projects focus on integrating the DICOM standard with the OHDSI common data model to create reproducible computational evidence generation. We receive funding from Gates Ventures as an unrestricted award to accelerate Alzheimer’s imaging research.