10 citations · 18 across the 5 of their papers we have counts for
5 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…
No Data? No Problem: Robust Vision-Tabular Learning with Missing Values
Marta Hasny, Laura Daza, Keno Bressem +2
Large-scale medical biobanks provide imaging data complemented by extensive tabular information, such as clinical measurements or demographics. However, this abundance of tabular a…
Tables Guide Vision: Learning to See the Heart through Tabular Data
Marta Hasny, Maxime Di Folco, Keno Bressem +1
Contrastive learning methods in computer vision typically rely on augmented views of the same image or multimodal pretraining strategies that align paired modalities. However, thes…
Vision Foundation Models for Computed Tomography
Suraj Pai, Ibrahim Hadzic, Dennis Bontempi +5
Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities. Here, we developed CT-FM, a large-scale 3D…
Generalist embedding models are better at short-context clinical semantic search than specialized embedding models
Jean-Baptiste Excoffier, Tom Roehr, Alexei Figueroa +3
The increasing use of tools and solutions based on Large Language Models (LLMs) for various tasks in the medical domain has become a prominent trend. Their use in this highly criti…