In the field of computer-and robot-assisted minimally invasive surgery, enormous progress
has been made in recent years based on the recognition of surgical instruments in …

Abstract Numerous Deep Learning (DL) classification models have been developed for a
large spectrum of medical image analysis applications, which promises to reshape various …

Formalizing surgical activities as triplets of the used instruments, actions performed, and
target anatomies is becoming a gold standard approach for surgical activity modeling. The …

Surgical instrument segmentation is fundamentally important for facilitating cognitive
intelligence in robot-assisted surgery. Although existing methods have achieved accurate …

Tracking and following objects of interest is critical to several robotics use cases, ranging
from industrial automation to logistics and warehousing, to healthcare and security. In this …

Strasberg's criteria to detect a critical view of safety is a widely known strategy to reduce bile
duct injuries during laparoscopic cholecystectomy. In spite of its popularity and efficiency …

This paper is a comprehensive survey of datasets for surgical tool detection and related
surgical data science and machine learning techniques and algorithms. The survey offers a …

Uncertainty estimation plays an important role for future reliable deployment of deep
segmentation models in safety-critical scenarios such as medical applications. However …

Recently, spatiotemporal graphs have emerged as a concise and elegant manner of
representing video clips in an object-centric fashion, and have shown to be useful for …

Videos are prominent learning materials to prepare surgical trainees before they enter the
operating room (OR). In this work, we explore techniques to enrich the video-based surgery …