3 papers
cs.CV2026
Segmentation of the aorta in 4D flow MRI using 4D convolutional kernels and learning from sparse annotations
Hinrich Rahlfs, Julio Garcia, Chiara Manini +13
Automated aortic segmentation in 4D flow MRI is essential for reproducible hemodynamic assessment but is limited by scarce dense annotations and high computational demands. We deve…
cs.IR2024
Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports
Malte Tölle, Lukas Burger, Halvar Kelm +21
Purpose: Federated training is often hindered by heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and dispar…
eess.IV2024
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging
Malte Tölle, Philipp Garthe, Clemens Scherer +22
Federated learning is a renowned technique for utilizing decentralized data while preserving privacy. However, real-world applications often face challenges like partially labeled…