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- Radboud University NijmegenNL242 papers
- Radboud Institute for Molecular Life SciencesNL139 papers
- Centre National de la Recherche ScientifiqueFR30 papers
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34 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…
Optimizing Federated Learning Configurations for MRI Prostate Segmentation and Cancer Detection: A Simulation Study
Ashkan Moradi, Fadila Zerka, Joeran S. Bosma +7
Purpose: To develop and optimize a federated learning (FL) framework across multiple clients for biparametric MRI prostate segmentation and clinically significant prostate cancer (…
crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
Navodini Wijethilake, Reuben Dorent, Marina Ivory +38
The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assist…
VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images
Jose Vargas Quiros, Bart Liefers, Karin van Garderen +4
We introduce VascX models, a comprehensive set of model ensembles for analyzing retinal vasculature from color fundus images (CFIs). Annotated CFIs were aggregated from public data…
Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Bi-parametric MRI Datasets
Hao Li, Han Liu, Heinrich von Busch +16
Our hypothesis is that UDA using diffusion-weighted images, generated with a unified model, offers a promising and reliable strategy for enhancing the performance of supervised lea…
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Hartmut Häntze, Lina Xu, Christian J. Mertens +29
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial…