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
5 papers · 1 filter
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…
Benchmarking Pathology Feature Extractors for Whole Slide Image Classification
Georg Wölflein, Dyke Ferber, Asier R. Meneghetti +6
Weakly supervised whole slide image classification is a key task in computational pathology, which involves predicting a slide-level label from a set of image patches constituting…
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
Soroosh Tayebi Arasteh, Christiane Kuhl, Marwin-Jonathan Saehn +3
Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable datasets, preferably from multi…
Large Language Models Streamline Automated Machine Learning for Clinical Studies
Soroosh Tayebi Arasteh, Tianyu Han, Mahshad Lotfinia +4
A knowledge gap persists between machine learning (ML) developers (e.g., data scientists) and practitioners (e.g., clinicians), hampering the full utilization of ML for clinical da…
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…