most citedA Discriminative Event Based Model for Alzheimer's Disease Progression Modeling

1 citations · 1 across the 2 of their papers we have counts for

collaborators

11 papers

cs.LG2020

Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations

Vikram Venkatraghavan, Stefan Klein, Lana Fani +5

Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In…

eess.IV2020

Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration

Wietske A. P. Bastiaansen, Melek Rousian, Régine P. M. Steegers-Theunissen +3

We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approac…

q-bio.PE202073 cited

TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data

Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +8

The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train…

eess.SP2019

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…

eess.IV201912 cited

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…

cs.CV2019

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…