106 citations · 204 across the 11 of their papers we have counts for
11 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…
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…
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…
Boosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
Jannik Endres, Oliver Hahn, Charles Corbière +3
Omnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360° field of view. Camera-based setups offer a cost-e…
Retrieval-Based Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios
Charles Corbière, Simon Roburin, Syrielle Montariol +2
While chain-of-thought (CoT) prompting improves reasoning in large language models, its effectiveness in vision-language models (VLMs) remains limited due to over-reliance on textu…
Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation
Mehdi Zayene, Jannik Endres, Albias Havolli +4
Despite progress in stereo depth estimation, omnidirectional imaging remains underexplored, mainly due to the lack of appropriate data. We introduce Helvipad, a real-world dataset…
Robust Deep Learning for Autonomous Driving
Charles Corbière
The last decade's research in artificial intelligence had a significant impact on the advance of autonomous driving. Yet, safety remains a major concern when it comes to deploying…
Confidence Estimation via Auxiliary Models
Charles Corbière, Nicolas Thome, Antoine Saporta +3
Reliably quantifying the confidence of deep neural classifiers is a challenging yet fundamental requirement for deploying such models in safety-critical applications. In this paper…
Addressing Failure Prediction by Learning Model Confidence
Charles Corbière, Nicolas Thome, Avner Bar-Hen +2
Assessing reliably the confidence of a deep neural network and predicting its failures is of primary importance for the practical deployment of these models. In this paper, we prop…
Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction
Charles Corbière, Hedi Ben-Younes, Alexandre Ramé +1
In this paper, we present a method to learn a visual representation adapted for e-commerce products. Based on weakly supervised learning, our model learns from noisy datasets crawl…