Biomedical Image Analysis Group
The Biomedical Image Analysis Group at the University of California, San Diego (biag) focuses on the design of computational algorithms to extract quantitative measures from biomedical data and other data sources. While our emphasis is on image data (e.g., obtained via magnetic resonance imaging, computed tomography, or microscopy) our past and current work includes data ranging from unstructured text, to tabular data, and genomics. The group is led by Marc Niethammer.
Our work is highly interdisciplinary and includes collaborators from a wide range of disciplines such as statistics, applied mathematics, radiology, surgery, and epidemiology. Consequently, we publish in venues ranging from clinical journals to medical conferences. Our success is the joint success with our collaborators.
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Congratulations to Lin Tian, Hastings Greer who are presenting their CVPR paper on gradient inverse consistent image registraton (GradICON) this week. GradICON is a new deep-learning-based image registration approach which only weakly regularizes the tranformations via a new gradient inverse consistency loss. Doing so results in state-of-the-art registration performance (as tested on 3D knee, lung, and brain images) without the need for extensive hyperparameter tuning.