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Found 5 papers, 3 papers with code
Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery
The use of multi-centric training data, experiments 6) and 7), improves the generalization capabilities of the models, bringing them beyond the level of independent mono-centric training and validation (experiments 1) and 2)).
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition
In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos.
CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods
This work introduces two novel public datasets for CD in continuous manufacturing processes.
Colonoscopy Navigation using End-to-End Deep Visuomotor Control: A User Study
Flexible endoscopes for colonoscopy present several limitations due to their inherent complexity, resulting in patient discomfort and lack of intuitiveness for clinicians.
Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures
Conclusion: In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on the Bypass40 gastric bypass dataset with multi-level annotations.
