60 citations · 215 across the 55 of their papers we have counts for
58 papers
XAI-CLIP: ROI-Guided Perturbation Framework for Explainable Medical Image Segmentation in Multimodal Vision-Language Models
Thuraya Alzubaidi, Sana Ammar, Maryam Alsharqi +2
Medical image segmentation is a critical component of clinical workflows, enabling accurate diagnosis, treatment planning, and disease monitoring. However, despite the superior per…
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…
FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification
Prajit Sengupta, Islem Rekik
Medical image classification requires not only high predictive performance but also interpretability to ensure clinical trust and adoption. Graph Neural Networks (GNNs) offer a pow…
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…
Multimodal Sheaf-based Network for Glioblastoma Molecular Subtype Prediction
Shekhnaz Idrissova, Islem Rekik
Glioblastoma is a highly invasive brain tumor with rapid progression rates. Recent studies have shown that glioblastoma molecular subtype classification serves as a significant bio…
NEURAL: Attention-Guided Pruning for Unified Multimodal Resource-Constrained Clinical Evaluation
Devvrat Joshi, Islem Rekik
The rapid growth of multimodal medical imaging data presents significant storage and transmission challenges, particularly in resource-constrained clinical settings. We propose NEU…
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…
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…
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…
Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision
Bobby Azad, Reza Azad, Sania Eskandari +4
Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning proble…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
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…