LABEL: Segmentation of the anatomy in large-scale datasets under noisy and imperfect labels (Annotation-efficient deep learning for automatic medical image segmentation, Wang et al. 2021)

MEDICARE: Accelerated Reconstruction- Dynamic multi-coil MRI acceleration using Implicit Neural Representations and temporal regularization (CineJENSE: Simultaneous Cine MRI Image Reconstruction and Sensitivity Map Estimation using Neural Representations, Hemidi et al. 2023, submitted to STACOM Workshop at MICCAI 2023)

MEDICARE: Motion Compensation - Unsupervised Deep Learning Registration in undersampled cardiac MRI K- space data for respiratory motion compensation (LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging, Küstner et al. 2021)

MEIDIC-VTACH: (Bachelor thesis) Detection of abnormal ventricular beatsin surface time-series data (Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT, Chen et al. 2022)

EchoScout: Evaluation of tissue deformations in the reconstruction of 3D volumes from freehand 2D ultrasound sequences (PDF with more details; Contact: christoph(at)echoscout.ai).

Philips Hamburg: (Master thesis) Coronary Artery Tree Segmentation from CT Angiography Data (PDF with more details; Contact: Hannes.Nickisch(at)philips.com).

Hereon: (Internship) Development of an algorithm for the automated analysis of CT data from metal hydride powder beds for hydrogen storage (PDF with more details; Contact: Gerd.Stahlkopf(at)hereon.de).