818 citations
- Erasmus University RotterdamNL40 papers
- Delft University of TechnologyNL32 papers
- University of CopenhagenDK21 papers
- Eindhoven University of TechnologyNL14 papers
- Erasmus MC Cancer InstituteNL13 papers
- Amsterdam University Medical CentersNL8 papers
- Harvard UniversityUS8 papers
- Leiden University Medical CenterNL8 papers
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- Radboud University Medical CenterNL7 papers
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8 papers · 1 filter
A Fast, Accurate Two-Step Linear Mixed Model for Genetic Analysis Applied to Repeat MRI Measurements
Qifan Yang, Gennady V. Roshchupkin, Wiro J. Niessen +4
Large-scale biobanks are being collected around the world in efforts to better understand human health and risk factors for disease. They often survey hundreds of thousands of indi…
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen +1
Segmenting tree structures is common in several image processing applications. In medical image analysis, reliable segmentations of airways, vessels, neurons and other tree structu…
Early Experiences with Crowdsourcing Airway Annotations in Chest CT
Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo +2
Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine le…
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Gerda Bortsova, Gijs van Tulder, Florian Dubost +5
Intracranial carotid artery calcification (ICAC) is a major risk factor for stroke, and might contribute to dementia and cognitive decline. Reliance on time-consuming manual annota…
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images
Isabel Pino Peña, Veronika Cheplygina, Sofia Paschaloudi +5
A method for automatically quantifying emphysema regions using High-Resolution Computed Tomography (HRCT) scans of patients with chronic obstructive pulmonary disease (COPD) that d…
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
Florian Dubost, Gerda Bortsova, Hieab Adams +4
We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a…