Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Recent developments in data science in general and machine learning in particular have
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …

Assembly101 is a new procedural activity dataset featuring 4321 videos of people
assembling and disassembling 101" take-apart" toy vehicles. Participants work without fixed …

The intraoperative activity of a surgeon has substantial impact on postoperative outcomes.
However, for most surgical procedures, the details of intraoperative surgical actions, which …

Most of state of the art methods applied on time series consist of deep learning methods that
are too complex to be interpreted. This lack of interpretability is a major drawback, as several …

Objective: To provide an overview of ML models and data streams utilized for automated
surgical phase recognition. Background: Phase recognition identifies different steps and …

The dominant paradigm for video-based action segmentation is composed of two steps: first,
compute low-level features for each frame using Dense Trajectories or a Convolutional …

Temporal action localization is crucial for understanding untrimmed videos. In this work, we
first identify two underexplored problems posed by the weak supervision for temporal action …

Assessing action quality is challenging due to the subtle differences between videos and
large variations in scores. Most existing approaches tackle this problem by regressing a …

The Internet has made several giant leaps over the years, from a fixed to a mobile Internet,
then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet goes far …