5 citations · 8 across the 10 of their papers we have counts for
4 papers · 1 filter
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…