203 citations · 334 across the 22 of their papers we have counts for
5 papers · 1 filter
RadMatch: Auditable Radiology Report Evaluation via Finding-Level Matching
Charles Corbière, Léo Machado, Aubin Charley +3
As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language model (LLM)-based metrics ar…
Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation
Théo Danielou, Antoine Saporta, Léo Alberge +1
Radiology foundation models learn transferable representations that can be adapted to new tasks by training only small layers on top of a frozen encoder. Dense prediction tasks suc…
Jolia: Concept-Level Vision-Language Alignment for 3D CT Contrastive Learning
Julien Khlaut, Charles Corbière, Baptiste Callard +9
Vision-language contrastive pretraining has become the dominant recipe for 3D medical foundation models, leveraging the large volumes of paired scans and reports produced in clinic…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models
Antoine Saporta, Baptiste Callard, Corentin Dancette +5
The rapid growth of medical imaging has fueled the development of Foundation Models (FMs) to reduce the growing, unsustainable workload on radiologists. While recent FMs have shown…