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

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 …

Real-time algorithms for automatically recognizing surgical phases are needed to develop
systems that can provide assistance to surgeons, enable better management of operating …

Automatic surgical phase recognition plays a vital role in robot-assisted surgeries. Existing
methods ignored a pivotal problem that surgical phases should be classified by learning …

This paper introduces SKiT, a fast Key information Transformer for phase recognition of
videos. Unlike previous methods that rely on complex models to capture long-term temporal …

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 …

Surgical tool presence detection and surgical phase recognition are two fundamental yet
challenging tasks in surgical video analysis as well as very essential components in various …

Surgical workflow recognition has numerous potential medical applications, such as the
automatic indexing of surgical video databases and the optimization of real-time operating …

Purpose Automatic segmentation and classification of surgical activity is crucial for providing
advanced support in computer-assisted interventions and autonomous functionalities in …

Background Artificial intelligence and computer vision have revolutionized laparoscopic
surgical video analysis. However, there is no multi-center study focused on deep learning …