5 papers
Federated Transformer-GNN for Privacy-Preserving Brain Tumor Localization with Modality-Level Explainability
Andrea Protani, Riccardo Taiello, Marc Molina Van Den Bosch +1
Deep learning models for brain tumor analysis require large and diverse datasets that are often siloed across healthcare institutions due to privacy regulations. We present a feder…
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…
Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI
Lorenzo Giusti, Ole Anton Werner, Riccardo Taiello +8
We present Federation of Agents (FoA), a distributed orchestration framework that transforms static multi-agent coordination into dynamic, capability-driven collaboration. FoA intr…
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…
Brain MRI Screening Tool with Federated Learning
Roman Stoklasa, Ioannis Stathopoulos, Efstratios Karavasilis +5
In clinical practice, we often see significant delays between MRI scans and the diagnosis made by radiologists, even for severe cases. In some cases, this may be caused by the lack…