162 citations
- Radboud University NijmegenNL17 papers
- Radboud University Medical CenterNL16 papers
- University Hospital and ClinicsUS6 papers
- Delft University of TechnologyNL4 papers
- Radboud Institute for Molecular Life SciencesNL4 papers
- Amsterdam UMC Location Vrije Universiteit AmsterdamNL3 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS2 papers
- Imperial College LondonGB2 papers
- InsermFR2 papers
- Massachusetts General HospitalUS2 papers
- National Cancer InstituteUS2 papers
- The Netherlands Cancer InstituteNL2 papers
7 papers · 1 filter
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
Lasse Hansen, Wiebke Heyer, Christoph Großbröhmer +51
Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress in the field. To date, the…
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
Hartmut Häntze, Lina Xu, Maximilian Rattunde +5
Computed tomography (CT) segmentation models often contain classes that are not currently supported by magnetic resonance imaging (MRI) segmentation models. In this study, we show…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Single Plane-Wave Imaging using Physics-Based Deep Learning
Georgios Pilikos, Chris L. de Korte, Tristan van Leeuwen +1
In plane-wave imaging, multiple unfocused ultrasound waves are transmitted into a medium of interest from different angles and an image is formed from the recorded reflections. The…
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer
Yoni Schirris, Efstratios Gavves, Iris Nederlof +2
We propose a Deep learning-based weak label learning method for analyzing whole slide images (WSIs) of Hematoxylin and Eosin (H&E) stained tumor tissue not requiring pixel-level or…
Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology
Johannes Lotz, Nick Weiss, Jeroen van der Laak +1
Purpose: In digital histopathology, virtual multi-staining is important for diagnosis and biomarker research. Additionally, it provides accurate ground-truth for various deep-learn…