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Found 5 papers, 3 papers with code
Transferring Relative Monocular Depth to Surgical Vision with Temporal Consistency
We show temporal consistency significantly improves supervised training alone when transferring to the low-data regime of endoscopy, and outperforms the prevalent self-supervision technique for this task.
SegMatch: A semi-supervised learning method for surgical instrument segmentation
Surgical instrument segmentation is recognised as a key enabler to provide advanced surgical assistance and improve computer assisted interventions.
Deep Reinforcement Learning Based System for Intraoperative Hyperspectral Video Autofocusing
In addition, we performed a blinded usability trial by having two neurosurgeons compare the system with different autofocus policies, and found our novel approach to be the most favourable, making our system a desirable addition for intraoperative HSI.
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset
The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines.
