60 citations · 215 across the 55 of their papers we have counts for
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UnifiedFL: A Dynamic Unified Learning Framework for Equitable Federation
Furkan Pala, Islem Rekik
Federated learning (FL) has emerged as a key paradigm for collaborative model training across multiple clients without sharing raw data, enabling privacy-preserving applications in…
CogGNN: Cognitive Graph Neural Networks in Generative Connectomics
Mayssa Soussia, Yijun Lin, Mohamed Ali Mahjoub +1
Generative learning has advanced network neuroscience, enabling tasks like graph super-resolution, temporal graph prediction, and multimodal brain graph fusion. However, current me…
Multi-Sensory Cognitive Computing for Learning Population-level Brain Connectivity
Mayssa Soussia, Mohamed Ali Mahjoub, Islem Rekik
The generation of connectional brain templates (CBTs) has recently garnered significant attention for its potential to identify unique connectivity patterns shared across individua…
GNN-based Unified Deep Learning
Furkan Pala, Islem Rekik
Deep learning models often struggle to maintain generalizability in medical imaging, particularly under domain-fracture scenarios where distribution shifts arise from varying imagi…
X-Node: Self-Explanation is All We Need
Prajit Sengupta, Islem Rekik
Graph neural networks (GNNs) have achieved state-of-the-art results in computer vision and medical image classification tasks by capturing structural dependencies across data insta…
GraphTreeGen: Subtree-Centric Approach to Efficient and Supervised Graph Generation
Yitong Luo, Islem Rekik
Brain connectomes, representing neural connectivity as graphs, are crucial for understanding brain organization but costly and time-consuming to acquire, motivating generative appr…
Self-supervised Graph Transformer with Contrastive Learning for Brain Connectivity Analysis towards Improving Autism Detection
Yicheng Leng, Syed Muhammad Anwar, Islem Rekik +2
Functional Magnetic Resonance Imaging (fMRI) provides useful insights into the brain function both during task or rest. Representing fMRI data using correlation matrices is found t…
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…
Replica Tree-based Federated Learning using Limited Data
Ramona Ghilea, Islem Rekik
Learning from limited data has been extensively studied in machine learning, considering that deep neural networks achieve optimal performance when trained using a large amount of…
FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNs
Christopher Adnel, Islem Rekik
Graph Neural Network (GNN) ushered in a new era of machine learning with interconnected datasets. While traditional neural networks can only be trained on independent samples, GNN…
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…
Contrastive Graph Learning for Population-based fMRI Classification
Xuesong Wang, Lina Yao, Islem Rekik +1
Contrastive self-supervised learning has recently benefited fMRI classification with inductive biases. Its weak label reliance prevents overfitting on small medical datasets and ta…
Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates
Oytun Demirbilek, Islem Rekik
Learning how to estimate a connectional brain template(CBT) from a population of brain multigraphs, where each graph (e.g., functional) quantifies a particular relationship between…
A Comparative Study of Machine Learning Methods for Predicting the Evolution of Brain Connectivity from a Baseline Timepoint
Şeymanur Aktı, Doğay Kamar, Özgür Anıl Özlü +4
Predicting the evolution of the brain network, also called connectome, by foreseeing changes in the connectivity weights linking pairs of anatomical regions makes it possible to sp…
Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data
Mohammed Amine Gharsallaoui, Islem Rekik
Graph neural networks (GNNs) have witnessed an unprecedented proliferation in tackling several problems in computer vision, computer-aided diagnosis, and related fields. While prio…
Predicting cognitive scores with graph neural networks through sample selection learning
Martin Hanik, Mehmet Arif Demirtaş, Mohammed Amine Gharsallaoui +1
Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In exi…
Graph Neural Networks in Network Neuroscience
Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik
Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain co…
Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network
Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik
Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal change…
Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph
Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik
Several works based on Generative Adversarial Networks (GAN) have been recently proposed to predict a set of medical images from a single modality (e.g, FLAIR MRI from T1 MRI). How…
Supervised Multi-topology Network Cross-diffusion for Population-driven Brain Network Atlas Estimation
Islem Mhiri, Mohamed Ali Mahjoub, Islem Rekik
Estimating a representative and discriminative brain network atlas (BNA) is a nascent research field in mapping a population of brain networks in health and disease. Although limit…
Machine Learning Methods for Brain Network Classification: Application to Autism Diagnosis using Cortical Morphological Networks
Ismail Bilgen, Goktug Guvercin, Islem Rekik
Autism spectrum disorder (ASD) affects the brain connectivity at different levels. Nonetheless, non-invasively distinguishing such effects using magnetic resonance imaging (MRI) re…