3 citations · 24 across the 46 of their papers we have counts for
9 papers · 1 filter
Multi-Objective Deep-Learning-based Biomechanical Deformable Image Registration with MOREA
Georgios Andreadis, Eduard Ruiz Munné, Thomas H. W. Bäck +2
When choosing a deformable image registration (DIR) approach for images with large deformations and content mismatch, the realism of found transformations often needs to be traded…
Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation
Alexander Chebykin, Peter A. N. Bosman, Tanja Alderliesten
Sharing synthetic medical images is a promising alternative to sharing real images that can improve patient privacy and data security. To get good results, existing methods for med…
Deep learning-based auto-segmentation of paraganglioma for growth monitoring
E. M. C. Sijben, J. C. Jansen, M. de Ridder +2
Volume measurement of a paraganglioma (a rare neuroendocrine tumor that typically forms along major blood vessels and nerve pathways in the head and neck region) is crucial for mon…
A Tournament of Transformation Models: B-Spline-based vs. Mesh-based Multi-Objective Deformable Image Registration
Georgios Andreadis, Joas I. Mulder, Anton Bouter +2
The transformation model is an essential component of any deformable image registration approach. It provides a representation of physical deformations between images, thereby defi…
MOREA: a GPU-accelerated Evolutionary Algorithm for Multi-Objective Deformable Registration of 3D Medical Images
Georgios Andreadis, Peter A. N. Bosman, Tanja Alderliesten
Finding a realistic deformation that transforms one image into another, in case large deformations are required, is considered a key challenge in medical image analysis. Having a p…
Evolutionary Neural Cascade Search across Supernetworks
Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman
To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery…