1 citations · 1 across the 7 of their papers we have counts for
7 papers
DP-KFC: Data-Free Preconditioning for Privacy-Preserving Deep Learning
Marc Molina Van den Bosch, Riccardo Taiello, Albert Sund Aillet +3
Differentially private optimization suffers from a fundamental geometric mismatch: deep networks have highly anisotropic loss landscapes, yet DP-SGD injects isotropic noise. Second…
Decoder-Free Supervoxel GNN for Accurate Brain-Tumor Localization in Multi-Modal MRI
Andrea Protani, Marc Molina Van Den Bosch, Lorenzo Giusti +6
Modern vision backbones for 3D medical imaging typically process dense voxel grids through parameter-heavy encoder-decoder structures, a design that allocates a significant portion…
Federated GNNs for EEG-Based Stroke Assessment
Andrea Protani, Lorenzo Giusti, Albert Sund Aillet +9
Machine learning (ML) has the potential to become an essential tool in supporting clinical decision-making processes, offering enhanced diagnostic capabilities and personalized tre…
Feasibility Analysis of Federated Neural Networks for Explainable Detection of Atrial Fibrillation
Diogo Reis Santos, Andrea Protani, Lorenzo Giusti +3
Early detection of atrial fibrillation (AFib) is challenging due to its asymptomatic and paroxysmal nature. However, advances in deep learning algorithms and the vast collection of…
A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers
Diogo Reis Santos, Albert Sund Aillet, Antonio Boiano +10
The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-makin…
Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals
Andrea Protani, Lorenzo Giusti, Chiara Iacovelli +9
After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Graph theory methods have shown that…
A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications
Antonio Boiano, Marco Di Gennaro, Luca Barbieri +7
Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. In this context, the TRUS…