Search Results for author:
Found 13 papers, 4 papers with code
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
In the literature, two types of features are typically used to perform this task: visual features and tool usage signals.
Ranked #5 on
Surgical tool detection
on Cholec80
Offline surgical phase recognition
Online surgical phase recognition
+2
Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data
Proposed methods for the operating room (OR) rely either on foreground estimation using a multi-camera system, which is a challenge in real ORs due to color similarities and frequent illumination changes, or on wearable sensors or markers, which are invasive and therefore difficult to introduce in the room.
Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016
On top of these architectures we propose to use two different approaches to enforce the temporal constraints of the surgical workflow: (1) HMM-based and (2) LSTM-based pipelines.
Single- and Multi-Task Architectures for Tool Presence Detection Challenge at M2CAI 2016
The tool presence detection challenge at M2CAI 2016 consists of identifying the presence/absence of seven surgical tools in the images of cholecystectomy videos.
A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms
In this paper, we propose an approach for multi-view 3D human pose estimation from RGB-D images and demonstrate the benefits of using the additional depth channel for pose refinement beyond its use for the generation of improved features.
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.
A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture
For the training of these networks, we analyze the use of two different color spaces: gray-scale and RGB data images.
Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy
Of these, two architectures are taken as core-models, namely U-Net based in residual blocks($m_1$) and Mask-RCNN($m_2$), which are fed with single still-frames $I(t)$.
A transfer-learning approach for lesion detection in endoscopic images from the urinary tract
In this work we study the implementation of 3 different Convolutional Neural Networks (CNNs), using a 2-steps training strategy, to classify images from the urinary tract with and without lesions.
Data Stream Stabilization for Optical Coherence Tomography Volumetric Scanning
The non-constant rotation speed of optical components in the OCT catheter tip causes rotational distortion in OCT volumetric scanning.
Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing
Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video.
Semi-supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images
We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i. e. there is no exact equivalent for every image in both NBI and WLI domains.
Spatiotemporal modeling of grip forces captures proficiency in manual robot control
This paper builds on our previous work by exploiting Artificial Intelligence to predict individual grip force variability in manual robot control.
