8 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…
Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation
Tristan Kirscher, Markus Bujotzek, Yannick Kirchhoff +5
Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-fold cross-validation (CV),…
Kaapana: A Comprehensive Open-Source Platform for Integrating AI in Medical Imaging Research Environments
Ünal 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…
Visual Prompt Engineering for Vision Language Models in Radiology
Stefan Denner, Markus Bujotzek, Dimitrios Bounias +3
Medical image classification plays a crucial role in clinical decision-making, yet most models are constrained to a fixed set of predefined classes, limiting their adaptability to…
Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain
Markus R. Bujotzek, Ünal 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…