15 citations · 17 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
A denoised Mean Teacher for domain adaptive point cloud registration
Alexander Bigalke, Mattias P. Heinrich
Point cloud-based medical registration promises increased computational efficiency, robustness to intensity shifts, and anonymity preservation but is limited by the inefficacy of u…
Anatomy-guided domain adaptation for 3D in-bed human pose estimation
Alexander Bigalke, Lasse Hansen, Jasper Diesel +3
3D human pose estimation is a key component of clinical monitoring systems. The clinical applicability of deep pose estimation models, however, is limited by their poor generalizat…
Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts
Alexander Bigalke, Lasse Hansen, Mattias P. Heinrich
Recent deep learning-based methods for medical image registration achieve results that are competitive with conventional optimization algorithms at reduced run times. However, deep…