26 citations · 46 across the 28 of their papers we have counts for
33 papers
Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning
Imen Jegham, Islem Rekik
Decrypting intelligence from the human brain construct is vital in the detection of particular neurological disorders. Recently, functional brain connectomes have been used success…
Deep Cross-Modality and Resolution Graph Integration for Universal Brain Connectivity Mapping and Augmentation
Ece Cinar, Sinem Elif Haseki, Alaa Bessadok +1
The connectional brain template (CBT) captures the shared traits across all individuals of a given population of brain connectomes, thereby acting as a fingerprint. Estimating a CB…
Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets
Mehmet Yigit Balik, Arwa Rekik, Islem Rekik
Graph neural networks (GNNs) have achieved extraordinary enhancements in various areas including the fields medical imaging and network neuroscience where they displayed a high acc…
Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification
Furkan Pala, Islem Rekik
A central challenge in training one-shot learning models is the limited representativeness of the available shots of the data space. Particularly in the field of network neuroscien…
Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mapping
Nada Chaari, Hatice Camgoz Akdag, Islem Rekik
One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fi…
One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction
Umut Guvercin, Mohammed Amine Gharsallaoui, Islem Rekik
Few-shot learning presents a challenging paradigm for training discriminative models on a few training samples representing the target classes to discriminate. However, classificat…