386 citations · 1.4k across the 15 of their papers we have counts for
16 papers
Semi-Supervised Segmentation via Embedding Matching
Weiyi Xie, Nathalie Willems, Nikolas Lessmann +2
Deep convolutional neural networks are widely used in medical image segmentation but require many labeled images for training. Annotating three-dimensional medical images is a time…
Transfer learning from a sparsely annotated dataset of 3D medical images
Gabriel Efrain Humpire-Mamani, Colin Jacobs, Mathias Prokop +2
Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance…
Kidney abnormality segmentation in thorax-abdomen CT scans
Gabriel Efrain Humpire Mamani, Nikolas Lessmann, Ernst Th. Scholten +3
In this study, we introduce a deep learning approach for segmenting kidney parenchyma and kidney abnormalities to support clinicians in identifying and quantifying renal abnormalit…
Lumbar spine segmentation in MR images: a dataset and a public benchmark
Jasper W. van der Graaf, Miranda L. van Hooff, Constantinus F. M. Buckens +6
This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (I…
CNN-based Lung CT Registration with Multiple Anatomical Constraints
Alessa Hering, Stephanie Häger, Jan Moltz +3
Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance…
Improving Automated COVID-19 Grading with Convolutional Neural Networks in Computed Tomography Scans: An Ablation Study
Coen de Vente, Luuk H. Boulogne, Kiran Vaidhya Venkadesh +5
Amidst the ongoing pandemic, several studies have shown that COVID-19 classification and grading using computed tomography (CT) images can be automated with convolutional neural ne…