  Surgical data science is revolutionizing minimally invasive surgery by
enabling context-aware applications. However, many challenges exist around
surgical data (and health data, more generally) needed to develop context-aware
models. This work - presented as part of the Endoscopic Vision (EndoVis)
challenge at the Medical Image Computing and Computer Assisted Intervention
(MICCAI) 2020 conference - seeks to explore the potential for visual domain
adaptation in surgery to overcome data privacy concerns. In particular, we
propose to use video from virtual reality (VR) simulations of surgical
exercises in robotic-assisted surgery to develop algorithms to recognize tasks
in a clinical-like setting. We present the performance of the different
approaches to solve visual domain adaptation developed by challenge
participants. Our analysis shows that the presented models were unable to learn
meaningful motion based features form VR data alone, but did significantly
better when small amount of clinical-like data was also made available. Based
on these results, we discuss promising methods and further work to address the
problem of visual domain adaptation in surgical data science. We also release
the challenge dataset publicly at https://www.synapse.org/surgvisdom2020.
