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6 papers · 1 filter
Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes
Lukas Galke, Iacopo Vagliano, Benedikt Franke +3
Lifelong graph learning deals with the problem of continually adapting graph neural network (GNN) models to changes in evolving graphs. We address two critical challenges of lifelo…
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction
D. Karkalousos, S. Noteboom, H. E. Hulst +2
Machine Learning methods can learn how to reconstruct Magnetic Resonance Images and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physi…
A Bayesian accelerated failure time model for interval censored three-state screening outcomes
Thomas Klausch, Eddymurphy U. Akwiwu, Mark A. van de Wiel +2
Women infected by the Human papilloma virus are at an increased risk to develop cervical intraepithalial neoplasia lesions (CIN). CIN are classified into three grades of increasing…
Prediction of the Facial Growth Direction is Challenging
Stanisław Kaźmierczak, Zofia Juszka, Vaska Vandevska-Radunovic +3
Facial dysmorphology or malocclusion is frequently associated with abnormal growth of the face. The ability to predict facial growth (FG) direction would allow clinicians to prepar…
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling
Ahmed El-Gazzar, Rajat Mani Thomas, Guido van Wingen
The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popu…
Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration
Monika Grewal, Jan Wiersma, Henrike Westerveld +2
Purpose: Deformable Image Registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudie…