Past Research Projects

Self-supervised representation learning for surgical activity recognition

Daniel Paysan, Luis Haug, Michael Bajka, Markus Oelhafen, Joachim M. Buhmann

Virtual reality-based simulators have the potential to become an essential part of surgical education. To make full use of this potential, they must be able to automatically recognize activities performed by users and assess those. Since annotations of trajectories by human experts are expensive, there is a need for methods that can learn to recognize surgical activities in a data-efficient way. Methods: We use self-supervised training of deep encoder–decoder architectures to learn representations of surgical trajectories from video data.