Medical imaging modalities combined with powerful image processing algorithms are emerging as an essential component of clinical routine to enable effective triage or guide minimally invasive treatment. Recent advances in computer vision, including leaps in machine learning systems and augmented reality technology, fuel cutting edge research on contextual and task-aware computer assistance solutions that cater to the physicians’ needs to enable improved clinical decision making. In this talk, I will highlight some of our recent work on human-centric end-to-end systems that assist clinicians in improving on-task performance, with examples ranging from task-aware image acquisition to dynamic augmented reality environments.
Mathias Unberath is an Assistant Research Professor in the Department of Computer Science at Johns Hopkins University with affiliations to the Laboratory for Computational Sensing and Robotics and the Malone Center for Engineering in Healthcare. He has created and is currently leading the ARCADE research group on Advanced Robotics and Computationally AugmenteD Environments, which focuses on computer vision, machine learning, and augmented reality and the application thereof to medical imaging, surgical robotics, and clinician-centric assistance systems. Previously, Mathias was a postdoctoral fellow in the Laboratory for Computational Sensing and Robotics at Hopkins and completed his PhD in Computer Science at the Friedrich-Alexander-Universität Erlangen-Nürnberg from which he graduated summa cum laude in 2017. While completing a Bachelor’s in Physics and Master’s in Optical Technologies at FAU Erlangen, Mathias studied at the University of Eastern Finland as ERASMUS scholar in 2011 and joined Stanford University as DAAD fellow throughout 2014.