6 papers · 1 filter
Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection
Markus Bujotzek, Dimitrios Bounias, Stefan Denner +4
While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site la…
An Open-Source Monitoring Framework for Data Exploration and Progress Tracking in Multi-Center Radiology Studies
Markus Bujotzek, Jonas Scherer, Stefan Denner +7
Multi-center studies are crucial for advancing medical and radiological research. Data exploration, collaboration discovery, and study progress monitoring are essential for maximiz…
Multimodal classification of Radiation-Induced Contrast Enhancements and tumor recurrence using deep learning
Robin Peretzke, Marlin Hanstein, Maximilian Fischer +15
The differentiation between tumor recurrence and radiation-induced contrast enhancements in post-treatment glioblastoma patients remains a major clinical challenge. Existing approa…
nnLandmark: A Self-Configuring Method for 3D Medical Landmark Detection
Alexandra Ertl, Stefan Denner, Robin Peretzke +8
Landmark detection is central to many medical applications, such as identifying critical structures for treatment planning or defining control points for biometric measurements. Ho…
Kaapana: A Comprehensive Open-Source Platform for Integrating AI in Medical Imaging Research Environments
Ãnal Akünal, Markus Bujotzek, Stefan Denner +8
Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, l…
Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain
Markus R. Bujotzek, Ãnal Akünal, Stefan Denner +17
Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments…