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Found 9 papers, 7 papers with code
TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition
To enable context-aware computer assistance in the operating room of the future, cognitive systems need to understand automatically which surgical phase is being performed by the medical team.
Metrics Matter in Surgical Phase Recognition
Surgical phase recognition is a basic component for different context-aware applications in computer- and robot-assisted surgery.
On the Pitfalls of Batch Normalization for End-to-End Video Learning: A Study on Surgical Workflow Analysis
In this paper, we analyze pitfalls of BN in video learning, including issues specific to online tasks such as a 'cheating' effect in anticipation.
Rethinking Anticipation Tasks: Uncertainty-aware Anticipation of Sparse Surgical Instrument Usage for Context-aware Assistance
We propose a novel learning task for anticipation of instrument usage in laparoscopic videos that overcomes these limitations.
Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video
However, surgical gesture recognition based only on video is a challenging problem that requires effective means to extract both visual and temporal information from the video.
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
We show that this data set can be used to train models for the task of liver segmentation of laparoscopic images.
Video-based surgical skill assessment using 3D convolutional neural networks
In contrast, we investigate a method for automatic, objective skill assessment that requires video data only.
Interactive health insight miner: an adaptive, semantic-based approach
E-health applications aim to support the user in adopting healthy habits.
Temporal coherence-based self-supervised learning for laparoscopic workflow analysis
To achieve this, methods for surgical workflow analysis are crucial.
