5 citations · 8 across the 10 of their papers we have counts for
10 papers
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…
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…
RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering
Léo Butsanets, Charles Corbière, Julien Khlaut +2
In this work, we introduce RadImageNet-VQA, a large-scale dataset designed to advance radiologic visual question answering (VQA) on CT and MRI exams. Existing medical VQA datasets…
Curia: A Multi-Modal Foundation Model for Radiology
Corentin Dancette, Julien Khlaut, Antoine Saporta +20
AI-assisted radiological interpretation is based on predominantly narrow, single-task models. This approach is impractical for covering the vast spectrum of imaging modalities, dis…
RAPS-3D: Efficient interactive segmentation for 3D radiological imaging
Théo Danielou, Daniel Tordjman, Pierre Manceron +1
Promptable segmentation, introduced by the Segment Anything Model (SAM), is a promising approach for medical imaging, as it enables clinicians to guide and refine model predictions…
RadSAM: Segmenting 3D radiological images with a 2D promptable model
Julien Khlaut, Elodie Ferreres, Daniel Tordjman +4
Medical image segmentation is a crucial and time-consuming task in clinical care, where mask precision is extremely important. The Segment Anything Model (SAM) offers a promising a…
ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation
Léo Machado, Hélène Philippe, Élodie Ferreres +12
Carcinogenesis is a proteiform phenomenon, with tumors emerging in various locations and displaying complex, diverse shapes. At the crucial intersection of research and clinical pr…
Efficient Medical Question Answering with Knowledge-Augmented Question Generation
Julien Khlaut, Corentin Dancette, Elodie Ferreres +3
In the expanding field of language model applications, medical knowledge representation remains a significant challenge due to the specialized nature of the domain. Large language…