1 citations · 1 across the 5 of their papers we have counts for
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CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration
Eytan Kats, Christoph Grossbroehmer, Ziad Al-Haj Hemidi +3
Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time points. However, intensity inconsiste…
PrIINeR: Towards Prior-Informed Implicit Neural Representations for Accelerated MRI
Ziad Al-Haj Hemidi, Eytan Kats, Mattias P. Heinrich
Accelerating Magnetic Resonance Imaging (MRI) reduces scan time but often degrades image quality. While Implicit Neural Representations (INRs) show promise for MRI reconstruction,…
Self-supervised Learning of Dense Hierarchical Representations for Medical Image Segmentation
Eytan Kats, Jochen G. Hirsch, Mattias P. Heinrich
This paper demonstrates a self-supervised framework for learning voxel-wise coarse-to-fine representations tailored for dense downstream tasks. Our approach stems from the observat…
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation
Christian Weihsbach, Christian N. Kruse, Alexander Bigalke +1
Purpose: Applying pre-trained medical deep learning segmentation models on out-of-domain images often yields predictions of insufficient quality. In this study, we propose to use a…