6 papers
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…
Bridging Radiology and Pathology: A DICOM-Based Framework for Multimodal Mapping and Integrated Visualization
Nilesh P. Rijhwani, Titus J. Brinker, Peter Neher +4
Accurate disease diagnosis depends on effective collaboration between medical specialties, yet departments often use distinct data systems and proprietary formats. This heterogenei…
LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging
Maximilian Rokuss, Yannick Kirchhoff, Seval Akbal +7
In this work, we present LesionLocator, a framework for zero-shot longitudinal lesion tracking and segmentation in 3D medical imaging, establishing the first end-to-end model capab…
Precision ICU Resource Planning: A Multimodal Model for Brain Surgery Outcomes
Maximilian Fischer, Florian M. Hauptmann, Robin Peretzke +4
Although advances in brain surgery techniques have led to fewer postoperative complications requiring Intensive Care Unit (ICU) monitoring, the routine transfer of patients to the…