4 citations · 9 across the 7 of their papers we have counts for
8 papers · 1 filter
Unsupervised 3D registration through optimization-guided cyclical self-training
Alexander Bigalke, Lasse Hansen, Tony C. W. Mok +1
State-of-the-art deep learning-based registration methods employ three different learning strategies: supervised learning, which requires costly manual annotations, unsupervised le…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation
Mattias P. Heinrich, Lasse Hansen
The majority of current research in deep learning based image registration addresses inter-patient brain registration with moderate deformation magnitudes. The recent Learn2Reg med…
Deep learning based geometric registration for medical images: How accurate can we get without visual features?
Lasse Hansen, Mattias P. Heinrich
As in other areas of medical image analysis, e.g. semantic segmentation, deep learning is currently driving the development of new approaches for image registration. Multi-scale en…
Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies
Mattias P Heinrich, Lasse Hansen
Multimodal image registration is a very challenging problem for deep learning approaches. Most current work focuses on either supervised learning that requires labelled training sc…
Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings
Lasse Hansen, Mattias P. Heinrich
Though, deep learning based medical image registration is currently starting to show promising advances, often, it still fells behind conventional frameworks in terms of registrati…