Prof. Mathias Unberath

Radiology Artificial Intelligence Lab
Laboratory for Computational Sensing and Robotics
Johns Hopkins University

Shape from Scoping – Self-supervised Depth Estimation and Reconstruction from Sinus Endoscopy

Recent advances in computer vision, including leaps in machine learning systems, fuel cutting edge research on contextual and task-aware computer assistance solutions that streamline workflows while catering to the physicians' needs to enable improved clinical decision making. In this talk, I will highlight some of our recent work aiming to push the boundaries in navigated sinus endoscopy. I will focus on self-supervised learning from monocular video without photometric constancy, demonstrating applications in monocular depth estimation, correspondence finding, and photo-realistic dense reconstruction. Bio: Mathias Unberath, Co-Director of the Radiology Artificial Intelligence Lab, is an Assistant Research Professor in the Department of Computer Science at Johns Hopkins University, and is affiliated with the Laboratory for Computational Sensing and Robotics and the Malone Center for Engineering in Healthcare. Mathias first joined Hopkins as a postdoctoral fellow after graduating summa cum laude from the Friedrich-Alexander-Universität Erlangen-Nürnberg with a BSc in Physics, a MSc in Optical Technologies, and a PhD in Computer Science. He was an ERASMUS scholar at the University of Eastern Finland and DAAD fellow at Stanford University. Mathias’ research at the intersection of computer vision including augmented reality, machine learning, and medical physics has been recognized with multiple national and international awards, and aims at pushing the boundaries of computer assistance in medical imaging and image-guided interventions.