4 papers
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…
Untangling Vascular Trees for Surgery and Interventional Radiology
Guillaume Houry, Tom Boeken, Stéphanie Allassonnière +1
The diffusion of minimally invasive, endovascular interventions motivates the development of visualization methods for complex vascular networks. We propose a planar representation…
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…
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…