[PDF][PDF] How to create the largest in-vivo endoscopic dataset
S Bittel, V Roethlingshoefer, H Kenngott… - … Stenting, and Large …, 2017 - labels.tue-image.nl
In this work, we present a novel approach to generate large amounts of training data for
supervised machine learning algorithms. Traditionally, labeling a high quantity of data …
supervised machine learning algorithms. Traditionally, labeling a high quantity of data …
Crowd-algorithm collaboration for large-scale endoscopic image annotation with confidence
With the recent breakthrough success of machine learning based solutions for automatic
image annotation, the availability of reference image annotations for algorithm training is …
image annotation, the availability of reference image annotations for algorithm training is …
Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention
This volume contains the proceedings of the 4th International Workshop on Large-scale
Annotation of Biomedical data and Expert Label Synthesis (LABELS 2019), which was held …
Annotation of Biomedical data and Expert Label Synthesis (LABELS 2019), which was held …
Towards an efficient way of building annotated medical image collections for big data studies
Annotating large collections of medical images is essential for building robust image
analysis pipelines for different applications, such as disease detection. This process …
analysis pipelines for different applications, such as disease detection. This process …
Intravascular Imaging and Computer Assisted Stenting, and large-scale annotation of biomedical data and expert label synthesis
MICCAI 2017 is again hosting the Joint MICCAI-Workshops on Computing and Visualization
for Intravascular Imaging and Computer Assisted Stenting (MICCAI CVII-STENT), focusing …
for Intravascular Imaging and Computer Assisted Stenting (MICCAI CVII-STENT), focusing …
A web-based platform for distributed annotation of computerized tomography scans
N Heller, P Stanitsas, V Morellas… - … Imaging and Computer …, 2017 - Springer
Abstract Computer Aided Diagnosis (CAD) systems are adopting advancements at the
forefront of computer vision and machine learning towards assisting medical experts with …
forefront of computer vision and machine learning towards assisting medical experts with …
[HTML][HTML] Going to extremes: weakly supervised medical image segmentation
Medical image annotation is a major hurdle for developing precise and robust machine-
learning models. Annotation is expensive, time-consuming, and often requires expert …
learning models. Annotation is expensive, time-consuming, and often requires expert …
Active, continual fine tuning of convolutional neural networks for reducing annotation efforts
The splendid success of convolutional neural networks (CNNs) in computer vision is largely
attributable to the availability of massive annotated datasets, such as ImageNet and Places …
attributable to the availability of massive annotated datasets, such as ImageNet and Places …
Crowdsourcing for reference correspondence generation in endoscopic images
L Maier-Hein, S Mersmann, D Kondermann… - … Image Computing and …, 2014 - Springer
Computer-assisted minimally-invasive surgery (MIS) is often based on algorithms that
require establishing correspondences between endoscopic images. However, reference …
require establishing correspondences between endoscopic images. However, reference …
Deep learning and convolutional neural networks for medical image computing
This book was partially motivated by the recent rapid progress on deep convolutional and
recurrent neural network models and the abundance of important applications in computer …
recurrent neural network models and the abundance of important applications in computer …