4 citations · 8 across the 4 of their papers we have counts for
6 papers
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…
Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients
Lasse Hansen, Doris Dittmer, Mattias P. Heinrich
Deformable registration continues to be one of the key challenges in medical image analysis. While iconic registration methods have started to benefit from the recent advances in m…
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds
Lasse Hansen, Jasper Diesel, Mattias P. Heinrich
Graph convolutional networks are a new promising learning approach to deal with data on irregular domains. They are predestined to overcome certain limitations of conventional grid…