60 citations · 215 across the 55 of their papers we have counts for
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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…
Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory
Alpay Tekin, Ahmed Nebli, Islem Rekik
Several brain disorders can be detected by observing alterations in the brain's structural and functional connectivities. Neurological findings suggest that early diagnosis of brai…
StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis
Islem Mhiri, Mohamed Ali Mahjoub, Islem Rekik
Synthesizing multimodality medical data provides complementary knowledge and helps doctors make precise clinical decisions. Although promising, existing multimodal brain graph synt…
A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint
Alaa Bessadok, Ahmed Nebli, Mohamed Ali Mahjoub +4
Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analys…
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…
Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning
Basar Demir, Alaa Bessadok, Islem Rekik
Accurate and automated super-resolution image synthesis is highly desired since it has the great potential to circumvent the need for acquiring high-cost medical scans and a time-c…
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…
Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping
Islem Mhiri, Ahmed Nebli, Mohamed Ali Mahjoub +1
Brain graph synthesis marked a new era for predicting a target brain graph from a source one without incurring the high acquisition cost and processing time of neuroimaging data. H…
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…
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain Connectivity
Megi Isallari, Islem Rekik
Brain image analysis has advanced substantially in recent years with the proliferation of neuroimaging datasets acquired at different resolutions. While research on brain image sup…
MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations
Islem Rekik, Mustafa Burak Gurbuz
With the recent technological advances, biological datasets, often represented by networks (i.e., graphs) of interacting entities, proliferate with unprecedented complexity and het…