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Found 14 papers, 9 papers with code
MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation
In this paper, we present the dataset, its annotations, as well as baseline results from several recent person detection and 2D/3D pose estimation methods.
Dissecting Self-Supervised Learning Methods for Surgical Computer Vision
Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7. 4% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%.
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Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images
Human pose estimation (HPE) is a key building block for developing AI-based context-aware systems inside the operating room (OR).
Artificial Intelligence in Surgery: Neural Networks and Deep Learning
Deep neural networks power most recent successes of artificial intelligence, spanning from self-driving cars to computer aided diagnosis in radiology and pathology.
Advancing Surgical VQA with Scene Graph Knowledge
We highlight that the primary limitation in the current surgical VQA systems is the lack of scene knowledge to answer complex queries.
Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation
2D/3D human pose estimation is needed to develop novel intelligent tools for the operating room that can analyze and support the clinical activities.
SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose Estimation
Unlike current state-of-the-art fully-supervised methods, our approach does not require any 2d or 3d ground-truth poses and uses only the multi-view input images from a calibrated camera setup and 2d pseudo poses generated from an off-the-shelf 2d human pose estimator.
Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
SurgVLP constructs a new contrastive learning objective to align video clip embeddings with the corresponding multiple text embeddings by bringing them together within a joint latent space.
Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room
Second, to address the domain shift and the lack of annotations, we propose a novel unsupervised domain adaptation method, called AdaptOR, to adapt a model from an in-the-wild labeled source domain to a statistically different unlabeled target domain.
Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach
Methods: We propose a comparison of 6 state-of-the-art face detectors on clinical data using Multi-View Operating Room Faces (MVOR-Faces), a dataset of operating room images capturing real surgical activities.
Neuro-Endo-Trainer-Online Assessment System (NET-OAS) for Neuro-Endoscopic Skills Training
The objective of the current study was to develop a modified version (Neuro-Endo-Trainer-Online Assessment System (NET-OAS)) by providing a stand-alone system with online evaluation and real-time feedback.
Jumpstarting Surgical Computer Vision
Methods: In this work, we employ self-supervised learning to flexibly leverage diverse surgical datasets, thereby learning taskagnostic representations that can be used for various surgical downstream tasks.
ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room
Surgical robotics holds much promise for improving patient safety and clinician experience in the Operating Room (OR).
Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image Segmentation
In this work, we investigate the application of contrastive learning to the domain of medical image analysis.
