11 citations · 13 across the 3 of their papers we have counts for
4 papers
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
Francesco Cremonesi, Marc Vesin, Sergen Cansiz +16
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, t…
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism
Aude Sportisse, Hugo Schmutz, Olivier Humbert +2
Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of ``informative'' labels,…
Don't fear the unlabelled: safe semi-supervised learning via simple debiasing
Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei
Semi-supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model performance. Even though the domain has received a considerable amount o…
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation
Clement Zotti, Zhiming Luo, Alain Lalande +2
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. Th…