Christian Weihsbach

According to our database, Christian Weihsbach authored at least 8 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Autocalibration for 3D Ultrasound Reconstruction in Infant Hip Dysplasia Screening.

Wiebke Heyer, Christian Weihsbach, Christoph Otte, Jürgen Lichtenstein, Sebastian Lippross, Mattias P. Heinrich, Lasse Hansen

Proceedings of the Bildverarbeitung für die Medizin 2025, 2025

Abstract: IM-MoCo - Self-supervised MRI Motion Correction using Motion-guided Implicit Neural Representations.

Ziad Al-Haj Hemidi, Christian Weihsbach, Mattias P. Heinrich

Proceedings of the Bildverarbeitung für die Medizin 2025, 2025

2024

AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep Learning.

Christian Weihsbach, Nora Vogt, Ziad Al-Haj Hemidi, Alexander Bigalke, Lasse Hansen, Julien Oster, Mattias P. Heinrich

Sensors, April, 2024

IM-MoCo: Self-supervised MRI Motion Correction Using Motion-Guided Implicit Neural Representations.

Ziad Al-Haj Hemidi, Christian Weihsbach, Mattias P. Heinrich

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

2023

DG-TTA: Out-of-domain medical image segmentation through Domain Generalization and Test-Time Adaptation.

Christian Weihsbach, Christian N. Kruse, Alexander Bigalke, Mattias P. Heinrich

CoRR, 2023

CineJENSE: Simultaneous Cine MRI Image Reconstruction and Sensitivity Map Estimation Using Neural Representations.

Ziad Al-Haj Hemidi, Nora Vogt, Lucile Quillien, Christian Weihsbach, Mattias P. Heinrich, Julien Oster

Proceedings of the Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers, 2023

2022

DeepSTAPLE: Learning to Predict Multimodal Registration Quality for Unsupervised Domain Adaptation.

Christian Weihsbach, Alexander Bigalke, Christian N. Kruse, Hellena Hempe, Mattias P. Heinrich

Proceedings of the Biomedical Image Registration - 10th International Workshop, 2022

XEdgeConv: Leveraging graph convolutions for efficient, permutation- and rotation-invariant dense 3D medical image segmentation.

Christian Weihsbach, Lasse Hansen, Mattias P. Heinrich

Proceedings of the Geometric Deep Learning in Medical Image Analysis, 2022