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 …

Objective: Surgical activity recognition is a fundamental step in computer-assisted
interventions. This paper reviews the state-of-the-art in methods for automatic recognition of …

Objective: To develop a deep learning model to automatically segment hepatocystic
anatomy and assess the criteria defining the critical view of safety (CVS) in laparoscopic …

Automatic surgical phase recognition is a challenging and crucial task with the potential to
improve patient safety and become an integral part of intra-operative decision-support …

In this paper we introduce OperA, a transformer-based model that accurately predicts
surgical phases from long video sequences. A novel attention regularization loss …

Real-time surgical phase recognition is a fundamental task in modern operating rooms.
Previous works tackle this task relying on architectures arranged in spatio-temporal order …

Automatic surgical workflow recognition is a key component for developing context-aware
computer-assisted systems in the operating theatre. Previous works either jointly modeled …

Data-driven computational approaches have evolved to enable extraction of information
from medical images with reliability, accuracy, and speed, which is already transforming …

Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical
fields. Its tremendous capacity for powerful data-driven problem-solving has generated …

Surgical data science (SDS) aims to improve the quality of interventional healthcare and its
value through the capture, organization, analysis, and modeling of procedural data. As data …