215 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- RWTH Aachen UniversityDE16 papers
- Technical University of MunichDE6 papers
- Else Kröner Fresenius Center for Digital HealthDE5 papers
- Heidelberg UniversityDE5 papers
- University Hospital HeidelbergDE5 papers
- National Center for Tumor DiseasesDE4 papers
- Charité - Universitätsmedizin BerlinDE3 papers
- Humboldt-Universität zu BerlinDE3 papers
- Imperial College LondonGB3 papers
- TUM KlinikumDE3 papers
- University Hospital Carl Gustav CarusDE3 papers
- Deutsches Herzzentrum MünchenDE2 papers
7 papers · 1 filter
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
Gustav Müller-Franzes, Firas Khader, Robert Siepmann +4
MRI and CT are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. Wh…
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…
From Text to Image: Exploring GPT-4Vision's Potential in Advanced Radiological Analysis across Subspecialties
Felix Busch, Tianyu Han, Marcus Makowski +3
The study evaluates and compares GPT-4 and GPT-4Vision for radiological tasks, suggesting GPT-4Vision may recognize radiological features from images, thereby enhancing its diagnos…
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
Soroosh Tayebi Arasteh, Alexander Ziller, Christiane Kuhl +6
Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are requ…
Diffusion Probabilistic Models beat GANs on Medical Images
Gustav Müller-Franzes, Jan Moritz Niehues, Firas Khader +8
The success of Deep Learning applications critically depends on the quality and scale of the underlying training data. Generative adversarial networks (GANs) can generate arbitrary…
Automatic vocal tract landmark localization from midsagittal MRI data
Mohammad Eslami, Christiane Neuschaefer-Rube, Antoine Serrurier
The various speech sounds of a language are obtained by varying the shape and position of the articulators surrounding the vocal tract. Analyzing their variations is crucial for un…
Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification
Christoph Haarburger, Michael Baumgartner, Daniel Truhn +5
Classification of malignancy for breast cancer and other cancer types is usually tackled as an object detection problem: Individual lesions are first localized and then classified…