17 citations · 25 across the 4 of their papers we have counts for
5 papers · 1 filter
Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning
Théo Moutakanni, Piotr Bojanowski, Guillaume Chassagnon +7
AI Foundation models are gaining traction in various applications, including medical fields like radiology. However, medical foundation models are often tested on limited tasks, le…
Towards Better Certified Segmentation via Diffusion Models
Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon +4
The robustness of image segmentation has been an important research topic in the past few years as segmentation models have reached production-level accuracy. However, like classif…
Exploring Deep Registration Latent Spaces
Théo Estienne, Maria Vakalopoulou, Stergios Christodoulidis +8
Explainability of deep neural networks is one of the most challenging and interesting problems in the field. In this study, we investigate the topic focusing on the interpretabilit…
AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia
Guillaume Chassagnon, Maria Vakalopoulou, Enzo Battistella +28
Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and rapidity. The standard of referenc…
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
Stergios Christodoulidis, Mihir Sahasrabudhe, Maria Vakalopoulou +4
Image registration and in particular deformable registration methods are pillars of medical imaging. Inspired by the recent advances in deep learning, we propose in this paper, a n…