12 citations · 12 across the 4 of their papers we have counts for
6 papers · 1 filter
Deep learning-based group-wise registration for longitudinal MRI analysis in glioma
Claudia Chinea Hammecher, Karin van Garderen, Marion Smits +8
Glioma growth may be quantified with longitudinal image registration. However, the large mass-effects and tissue changes across images pose an added challenge. Here, we propose a l…
Evaluating glioma growth predictions as a forward ranking problem
Karin A. van Garderen, Sebastian R. van der Voort, Maarten M. J. Wijnenga +6
The problem of tumor growth prediction is challenging, but promising results have been achieved with both model-driven and statistical methods. In this work, we present a framework…
Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
Esther E. Bron, Stefan Klein, Janne M. Papma +14
This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of…
WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning
Sebastian R. van der Voort, Fatih Incekara, Maarten M. J. Wijnenga +14
Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is a time-consuming tas…
Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging
Bo Li, Marius de Groot, Rebecca M. E. Steketee +7
Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important…
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…