60 citations · 215 across the 55 of their papers we have counts for
9 papers · 1 filter
Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification
Oben Özgür, Arwa Rekik, Islem Rekik
The challenges of collecting medical data on neurological disorder diagnosis problems paved the way for learning methods with scarce number of samples. Due to this reason, one-shot…
Predicting Shape Development: a Riemannian Method
Doğa Türkseven, Islem Rekik, Christoph von Tycowicz +1
Predicting the future development of an anatomical shape from a single baseline observation is a challenging task. But it can be essential for clinical decision-making. Research ha…
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…
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…
Transformers in Medical Image Analysis: A Review
Kelei He, Chen Gan, Zhuoyuan Li +7
Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also…