5 papers
Robust Surgical Phase Recognition From Annotation Efficient Supervision
Or Rubin, Shlomi Laufer
Surgical phase recognition is a key task in computer-assisted surgery, aiming to automatically identify and categorize the different phases within a surgical procedure. Despite sub…
CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers
Shahaf Arica, Or Rubin, Sapir Gershov +1
In this paper, we introduce VoteCut, an innovative method for unsupervised object discovery that leverages feature representations from multiple self-supervised models. VoteCut emp…
Depth Over RGB: Automatic Evaluation of Open Surgery Skills Using Depth Camera
Ido Zuckerman, Nicole Werner, Jonathan Kouchly +4
Purpose: In this paper, we present a novel approach to the automatic evaluation of open surgery skills using depth cameras. This work is intended to show that depth cameras achieve…
SFGANS Self-supervised Future Generator for human ActioN Segmentation
Or Berman, Adam Goldbraikh, Shlomi Laufer
The ability to locate and classify action segments in long untrimmed video is of particular interest to many applications such as autonomous cars, robotics and healthcare applicati…
More Than Meets the Eye: Analyzing Anesthesiologists' Visual Attention in the Operating Room Using Deep Learning Models
Sapir Gershov, Fadi Mahameed, Aeyal Raz +1
Patient's vital signs, which are displayed on monitors, make the anesthesiologist's visual attention (VA) a key component in the safe management of patients under general anesthesi…