Theory and Methods Development
Medical Imaging Analysis
Medical images are a rich high-dimensional data source that can be used to help with tasks such as disease detection, monitoring, and characterization. The emergence of statistical learning and deep learning in particular has led to increased interest in medical imaging analysis. Several faculty members work on medical imaging analysis. For example, Dr. Lorin Crawford works on developing methods for identifying the physical features of 3D shapes that best explain variation between groups. Dr. Eloyan develops machine learning methods for tumor heterogeneity estimation, methods for brain functional connectivity, and biomarker estimation approaches for images collected from people with Alzheimer’s disease. Drs. Eloyan, Gatsonis, Duan, and Steingrimsson all work on investigating the statistical aspects of deep learning as well as methods for estimation and implementation of radiomics in imaging.