Search Results for author:
Found 14 papers, 1 papers with code
AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space
Our method's effectiveness is validated through extensive experiments on benchmark datasets such as DomainNet, Office-Home, and VisDA-C, where AdaEmbed consistently outperforms all the baselines, setting a new state of the art for SSDA.
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge
Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems.
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).
Tracking and Mapping in Medical Computer Vision: A Review
In this review, we provide an update to the field of camera-based tracking and scene mapping in surgery and diagnostics in medical computer vision.
Surgical Tattoos in Infrared: A Dataset for Quantifying Tissue Tracking and Mapping
STIR comprises hundreds of stereo video clips in both in-vivo and ex-vivo scenes with start and end points labelled in the IR spectrum.
M$^{3}$3D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding
We present a new pre-training strategy called M$^{3}$3D ($\underline{M}$ulti-$\underline{M}$odal $\underline{M}$asked $\underline{3D}$) built based on Multi-modal masked autoencoders that can leverage 3D priors and learned cross-modal representations in RGB-D data.
SurgMAE: Masked Autoencoders for Long Surgical Video Analysis
There has been a growing interest in using deep learning models for processing long surgical videos, in order to automatically detect clinical/operational activities and extract metrics that can enable workflow efficiency tools and applications.
SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Tracking
SENDD enables multiple downstream applications that require estimation of 3D motion in stereo endoscopy.
Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis
Data-driven approaches to assist operating room (OR) workflow analysis depend on large curated datasets that are time consuming and expensive to collect.
Adaptation of Surgical Activity Recognition Models Across Operating Rooms
We propose a new domain adaptation method to improve the performance of the surgical activity recognition model in a new operating room for which we only have unlabeled videos.
An Empirical Study on Activity Recognition in Long Surgical Videos
In this paper, we investigate many state-of-the-art backbones and temporal models to find architectures that yield the strongest performance for surgical activity recognition.
Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study
Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis.
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery
Automatic recognition of surgical activities in the operating room (OR) is a key technology for creating next generation intelligent surgical devices and workflow monitoring/support systems.
A Robotic 3D Perception System for Operating Room Environment Awareness
Purpose: We describe a 3D multi-view perception system for the da Vinci surgical system to enable Operating room (OR) scene understanding and context awareness.
