38 citations · 48 across the 15 of their papers we have counts for
16 papers
nnFoundation: 3D Foundation Models for Radiology
Constantin Ulrich Harsy, Tassilo Wald, Karol Gotkowski +80
Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise…
Resolution Meets Reduction: Efficient Visual Context for 3D Radiology Report Generation
Jonathan Suprijadi, Raphael Stock, Moritz Langenberg +10
Vision-language models offer a promising path toward automating radiology report generation, but applying them to full 3D CT volumes poses substantial computational challenges. Mod…
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…
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…
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…
Decoupling Semantic Similarity from Spatial Alignment for Neural Networks
Tassilo Wald, Constantin Ulrich, Gregor Köhler +6
What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions…
Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
Maximilian Rokuss, Yannick Kirchhoff, Saikat Roy +9
Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across tim…
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…
Automated Ensemble Multimodal Machine Learning for Healthcare
Fergus Imrie, Stefan Denner, Lucas S. Brunschwig +2
The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approac…
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…
Automatic classification of prostate MR series type using image content and metadata
Deepa Krishnaswamy, Bálint Kovács, Stefan Denner +6
With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial…
Leveraging Foundation Models for Content-Based Image Retrieval in Radiology
Stefan Denner, David Zimmerer, Dimitrios Bounias +8
Content-based image retrieval (CBIR) has the potential to significantly improve diagnostic aid and medical research in radiology. However, current CBIR systems face limitations due…
Efficient Large Scale Medical Image Dataset Preparation for Machine Learning Applications
Stefan Denner, Jonas Scherer, Klaus Kades +7
In the rapidly evolving field of medical imaging, machine learning algorithms have become indispensable for enhancing diagnostic accuracy. However, the effectiveness of these algor…
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study
Christoph Baur, Stefan Denner, Benedikt Wiestler +2
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these work…
Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
Stefan Denner, Ashkan Khakzar, Moiz Sajid +4
Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. We hypothesize that the spatio-temporal cu…