60 citations · 215 across the 55 of their papers we have counts for
4 papers · 1 filter
Strongly Topology-preserving GNNs for Brain Graph Super-resolution
Pragya Singh, Islem Rekik
Brain graph super-resolution (SR) is an under-explored yet highly relevant task in network neuroscience. It circumvents the need for costly and time-consuming medical imaging data…
UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks
Atefe Hassani, Islem Rekik
A fundamental challenge in federated learning lies in mixing heterogeneous datasets and classification tasks while minimizing the high communication cost caused by clients as well…
Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios
Geng Chen, Qingyue Wang, Islem Rekik
A connectional brain template (CBT) is a holistic representation of a population of multi-view brain connectivity graphs, encoding shared patterns and normalizing typical variation…
Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data
Michalis Pistos, Gang Li, Weili Lin +2
The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity developme…