41 citations · 129 across the 48 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint
Ahmed Nebli, Ugur Ali Kaplan, Islem Rekik
Learning how to predict the brain connectome (i.e. graph) development and aging is of paramount importance for charting the future of within-disorder and cross-disorder landscape o…