31 citations · 69 across the 9 of their papers we have counts for
9 papers
MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging
Leonard Nürnberg, Dennis Bontempi, Suraj Pai +17
Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are…
DBT-DINO: Towards Foundation model based analysis of Digital Breast Tomosynthesis
Felix J. Dorfner, Manon A. Dorster, Ryan Connolly +9
Foundation models have shown promise in medical imaging but remain underexplored for three-dimensional imaging modalities. No foundation model currently exists for Digital Breast T…
Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
Manon A. Dorster, Felix J. Dorfner, Mason C. Cleveland +6
As early detection of breast cancer strongly favors successful therapeutic outcomes, there is major commercial interest in optimizing breast cancer screening. However, current risk…
Scalable Drift Monitoring in Medical Imaging AI
Jameson Merkow, Felix J. Dorfner, Xiyu Yang +6
The integration of artificial intelligence (AI) into medical imaging has advanced clinical diagnostics but poses challenges in managing model drift and ensuring long-term reliabili…
Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data
Felix J. Dorfner, Amin Dada, Felix Busch +8
Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this appro…
Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
Felix J. Dorfner, Janis L. Vahldiek, Leonhard Donle +15
Purpose: To examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression. Methods: This r…
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Hartmut Häntze, Lina Xu, Christian J. Mertens +29
Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial…
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
Hartmut Häntze, Lina Xu, Maximilian Rattunde +5
Computed tomography (CT) segmentation models often contain classes that are not currently supported by magnetic resonance imaging (MRI) segmentation models. In this study, we show…
Is Open-Source There Yet? A Comparative Study on Commercial and Open-Source LLMs in Their Ability to Label Chest X-Ray Reports
Felix J. Dorfner, Liv Jürgensen, Leonhard Donle +11
Introduction: With the rapid advances in large language models (LLMs), there have been numerous new open source as well as commercial models. While recent publications have explore…