39 citations · 59 across the 5 of their papers we have counts for
5 papers
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…
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…
mvHOTA: A multi-view higher order tracking accuracy metric to measure spatial and temporal associations in multi-point detection
Lalith Sharan, Halvar Kelm, Gabriele Romano +3
Multi-point tracking is a challenging task that involves detecting points in the scene and tracking them across a sequence of frames. Computing detection-based measures like the F-…
Point detection through multi-instance deep heatmap regression for sutures in endoscopy
Lalith Sharan, Gabriele Romano, Julian Brand +4
Purpose: Mitral valve repair is a complex minimally invasive surgery of the heart valve. In this context, suture detection from endoscopic images is a highly relevant task that pro…
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation
Lalith Sharan, Gabriele Romano, Sven Koehler +4
The CycleGAN framework allows for unsupervised image-to-image translation of unpaired data. In a scenario of surgical training on a physical surgical simulator, this method can be…