100 citations · 289 across the 29 of their papers we have counts for
8 papers · 1 filter
An Efficient Method for Multi-Parameter Mapping in Quantitative MRI using B-Spline Interpolation
Willem van Valenberg, Stefan Klein, Frans M. Vos +3
Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equat…
Towards continuous learning for glioma segmentation with elastic weight consolidation
Karin van Garderen, Sebastian van der Voort, Fatih Incekara +2
When finetuning a convolutional neural network (CNN) on data from a new domain, catastrophic forgetting will reduce performance on the original training data. Elastic Weight Consol…
Multi-modal segmentation with missing MR sequences using pre-trained fusion networks
Karin van Garderen, Marion Smits, Stefan Klein
Missing data is a common problem in machine learning and in retrospective imaging research it is often encountered in the form of missing imaging modalities. We propose to take int…
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network
Chaoping Zhang, Florian Dubost, Marleen de Bruijne +2
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions. However, its dependence on representative training data limits the application a…
A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes
Bo Li, Wiro Niessen, Stefan Klein +4
To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often in…
Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation
Egor Panfilov, Aleksei Tiulpin, Stefan Klein +2
Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA) research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data is…