Journal & Conference Papers

2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024

2024 (11)

    1. C Adnel and I Rekik. FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNs. Journal of IEEE Transactions on Neural Networks and Learning Systems, IF: 10.2 (2024).
    2. K Cengiz and I Rekik. Cortical morphological networks for profiling autism spectrum disorder using tensor component analysis, Journal of Frontiers in Neurology, IF: 3.55 (2024).
    3. K Mancini and I Rekik. DuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-Decoupling. Workshop on GRaphs in biomedicAl Image anaLysis (GRAIL), International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct. 6-11, 2024, Marrakesh. (Oral presentation) [Code] [YouTube]
    4. M Soussia, M.A. Mahjoub and I Rekik. Generative Hypergraph Neural Network for Multiview Brain Connecitivity Fusion. Workshop on Predictive Intelligence in Medicine (PRIME), International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct. 6-11, 2024, Marrakesh. (Oral presentation) [Code] [YouTube]
    5. S. Xiao and I. Rekik. DynGNN: Dynamic Memory-enhanced Generative GNNs for Predicting Temporal Brain Connectivity. Workshop on Predictive Intelligence in Medicine (PRIME), International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct. 6-11, 2024, Marrakesh. [Code]
    6. P. Singh and I. Rekik. Strongly Topology-preserving GNNs for Brain Graph Super-resolution. Workshop on Predictive Intelligence in Medicine (PRIME), International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct. 6-11, 2024, Marrakesh. [Code] [YouTube]
    7. A. Hassani and I. Rekik. UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Tasks. Workshop on Machine Learning in Medical Imaging (MLMI), International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct. 6-11, 2024, Marrakesh. [Code] [YouTube]
    8. J. Liu, F. Pala, D. Shen and I. Rekik. DHSampling: Diversity-based Hyperedge Sampling in GNN Learning with Application to Medical Imaging Classification. Workshop on Machine Learning in Medical Imaging (MLMI), International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct. 6-11, 2024, Marrakesh. [Code] [YouTube]
    9. Liu, F. Liu, K. Sun, Y. Sun, J. Huang, C. Jiang, I. Rekik and D. Shen. UinTSeg: Unified Infant Brain Tissue Segmentation with Anatomy Delineation. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct. 6-11, 2024, Marrakesh.
    10. K. Belghiti, I. Rekik, S. Selim, M. Mounia and M. Rhanoui. Spatial Attention-Enhanced Diffusion Model for Multiple Sclerosis MRI Synthesis. Workshop on “Empowering MEdical information computing & Research through early-career Expertise” (EMERGE), International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Oct. 6-11, 2024, Marrakesh.
    11. H Jiang, S Zhang, X Wen, H Cui, J Lu, I Rekik, J Ma, G Chen, “Self-Supervised Denoising of Diffusion MRI Data Via Spatio-Angular Noise2Noise”, 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1-5.

2023 (13 + 1 arXiv)

    1. Kurucu MC and I. Rekik. Graph Neural Network based Unsupervised Influential Sample Selection for Brain Multigraph Population Fusion. Journal of  Computerized Medical Imaging and Graphics, IF: 7.42 (2023).
    2. K. He, C. Gan,  Z. Li, I. Rekik, Z. Yin, W. Ji, Y. Gao, Q. Wang, J. Zhang and D. Shen. Transformers in medical image analysis. Intelligent Medicine, IF: 4.4 (2023).
    3. B. Azad, R. Azad, S. Eskandari, A. Bozorgpour, A. Kazerouni, I. Rekik, D. Merhof . Foundational models in medical imaging: A comprehensive survey and future vision. arXiv preprint arXiv:2310.18689. 2023 Oct 28.
    4. D. Proios, A. Yazdani, A. Bornet, J. Ehrsam , I. Rekik, D. Teodoro. Leveraging patient similarities via graph neural networks to predict phenotypes from temporal data. In IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA) 2023 Oct 9 (pp. 1-10). IEEE.
    5. O. Ozgur, A. Rekik and I. Rekik. Deep learning brain connectivity augmentation for differentiating Alzheimer’s disease from mild cognitive impairment using limited data. Journal of the Neurological Sciences 455, IF: 3.03 (2023).
    6. J. Jia and I. Rekik. Federated Multimodal and Multiresolution Graph Integration for Connectional Brain Learning. DGM4MICCAI MICCAI (2023), Vancouver (Oral Presentation).  Check out the YouTube Video on Fed2M and our Fed2M code.
    7. C. Adnel and I. Rekik. Affordable Graph Neural Network Framework using Topological Graph Contraction. MILLanD MICCAI (2023), Vancouver (Oral Presentation).  Check out the YouTube Video on CQSIGN and our CQSIGN code.
    8. E. Gündoğdu and I. Rekik. Template-Based Federated Multiview Domain Alignment for Predicting Heterogeneous Brain Graph Evolution Trajectories from Baseline. PRIME MICCAI (2023), Vancouver (Oral Presentation).  Check out the YouTube Video on TAF-GNN and our TAF-GNN code.
    9. R. Ghilea and I. Rekik. Replica-Based Federated Learning with Heterogeneous Architectures for Graph Super-Resolution. MLMI MICCAI (2023), Vancouver (Oral Presentation).  Check out the YouTube Video on RepFL and our RepFL code.
    10. M. Pistos, G. Li, W. Lin, D. Shen and I. Rekik. Federated Multi-trajectory GNNs Under Data Limitations for Baby Brain Connectivity Forecasting. PRIME MICCAI (2023), Vancouver.  Check out the YouTube Video on FedGmTE-Net and our FedGmTE-Net code.
    11. N. Rajadhyaksha and I. Rekik. Diffusion-Based Graph Super-Resolution with Application to Connectomics. PRIME MICCAI (2023), Vancouver.  Check out the YouTube Video on Dif-GSR and our Dif-GSR code.
    12. C. Xu and I. Rekik. Federated Multi-domain GNN Network for Brain Multigraph Generation. PRIME MICCAI (2023), Vancouver.  Check out the YouTube Video on FMDGNN and our FMDGNN code.
    13. HC Bayram, MS Çelebi, and I. Rekik. RepNet for Quantifying the Reproducibility of Graph Neural Networks in Multiview Brain Connectivity Biomarker Discovery. PRIME MICCAI (2023), Vancouver.  Check out our RepNet code.
    14. D Türkseven, I. Rekik, C. von Tycowic and M. Hanik. Predicting Shape Development: A Riemannian Method. ShapeMI MICCAI (2023), Vancouver. Check out our ShapePrediction code.

2022 (22)

    1. X. Wang, L. Yao, I. Rekik, and Y. Zhang. Contrastive functional connectivity graph learning for population-based fMRI classification; MICCAI (2022), pp. 221-230. Cham: Springer Nature Switzerland, 2022.
    2. N. Chaari, H. Camgöz Akdağ and I. Rekik. Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mapping. Journal of Medical Image Analysis, IF: 13.82 (2022). Check out the GitHub repo of the surveyed methods.
    3. Z.Gurler and I. Rekik. Federated Brain Graph Evolution Prediction Using Decentralized Connectivity Datasets With Temporally-Varying Acquisitions. Journal of IEEE Transactions on MEdical Imaging, IF: 11.03 (2022). Check out the 4D-Fed-GNN-Plus GitHub code in Python and YouTube video.
    4. Z. Gurler, MA Gharsallaoui and I. Rekik. Template-Based Graph Registration Network for Boosting the Diagnosis of Brain Connectivity Disorders. Journal of Computerized Medical Imaging and Graphics, IF: 7.42 (2022). Check out the GRN GitHub code in Python. 
    5. O. Demirbilek and I. Rekik. Predicting the evolution trajectory of population-driven connectional brain templates using recurrent multigraph neural networks. Journal of Medical Image Analysis, IF: 13.82 (2022). Check out the YouTube Video on ReMI-Net-Star, the video demo and ReMI-Net-Star GitHub code in Python. 
    6. Bessadok, A., Mahjoub, M. A. and I. Rekik. Graph Neural Networks in Network Neuroscience. Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IF: 24.31 (2022). GitHub repo.
    7. F.S. Duran, A Beyaz and I. Rekik. Dual-HINet: Dual Hierarchical Integration Network of Multigraphs for Connectional Brain Template Learning. MICCAI (2022), Singapore. Check out the YouTube Video on Dual-HINet and our Dual-HINet code. Singapore (acceptance rate ~30%).
    8. X. Wang, L. Yao, I. Rekik and Y. Zhang. Contrastive Graph Learning for Population-based fMRI Classification. MICCAI (2022), Singapore (acceptance rate ~30%).
    9. I. Jegham and I. Rekik. Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning. PRIME MICCAI (2022), Singapore (Oral Presentation, MICCAI Student Travel Award).  Check out the YouTube Video on Meta-RegGNN, the video demo and our Meta-RegGNN code.
    10. F. Pala and I. Rekik. Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification. PRIME MICCAI (2022), Singapore. Check out the YouTube Video on MultigraphGNet, the video demo and our MultigraphGNet code.
    11. E Cinar, S.E. Haseki, A. Bessadok and I. Rekik. Deep Cross-Modality and Resolution Graph Integration for Universal Brain Connectivity Mapping and Augmentation. GRAIL MICCAI (2022), Singapore . Check out the YouTube Video on M2GraphIntegrator.
    12. M.Y. Balik, A. Rekik  and I. Rekik. Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets. PRIME MICCAI (2022), Singapore (Oral Presentation, MICCAI Student Travel). Check out the YouTube Video on reproducibleFedGNN, the video demo and our reproducibleFedGNN code.
    13. Z. Gurler and I. Rekik. Federated Time-dependent GNN Learning from Brain Connectivity Data with Missing Timepoints. PRIME MICCAI (2022), Singapore. Check out the YouTube Video on 4DFedGNN, the video demo and our 4DFedGNN code.
    14. S. Yurekli, M.A Demirtas and I. Rekik. Quantifying the Predictive Uncertainty of Regression GNN Models Under Target Domain Shifts. PRIME MICCAI (2022), Singapore (MICCAI Student Travel Award). Check out the YouTube Video on GNN predictive uncertainty, the video demo and our code.
    15. A. Rekik, M. Aissi, I. Rekik, M. Mhiri, and M.A Frih. Brain atrophy patterns in multiple sclerosis patients treated with natalizumab and its clinical correlates. Brain and Behavior, 12(5), p.e2573,  IF: 2.7 (2022).
    16. N. Chaari, M.A. Gharsallaoui, H. Camgöz Akdağ and I. Rekik. Multigraph Classification using Learnable Integration Network with Application to Gender Fingerprinting.  Journal of Neural Networks, IF: 9.65 (2022).
    17. Y. Wang, Q. Yang, L Tian, X Zhou, I. Rekik, and H. Huang. HFCF-Net: A Hybrid-Feature Cross Fusion Network for COVID-19 Lesion Segmentation from CT Volumetric Images, Medical Physics, IF: 4.07 (2022).
    18. E Onemli, S Joof, C Aydinalp, N Pastacı Özsobacı, F Ateş Alkan, N Kepil, I. Rekik, I Akduman, and T Yilmaz.  Classification of rat mammary carcinoma with large scale in vivo microwave measurements. Nature Scientific Reports, IF: 4.37  (2022).

2021 (20 + 1 arXiv)

    1. G. Chen, J. Ru, Yilin Zhou, I. Rekik, Z. Pan, X. Liu, Y. Lin, B. Lu and J. Shi. MTANS: Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation, Neuroimage, IF: 6.55 (2021).
    2. A. Nebli, M.A. Gharsallaoui, Z Gurler and I. Rekik. Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data. Journal of Neural Networks, IF: 9.65 (2021). Check out the RG-Select source code on GitHub.
    3. S. Akti, D. Kamar, Ö.A. Özlü, I. Soydemir, M. Akcan, A. Kul and I. Rekik. A Comparative Study of Machine Learning Methods for Predicting the Evolution of Brain Connectivity from a Baseline Timepoint. Journal of Neuroscience Methods, IF: 2.78 (2021). Check out the 20 Kaggle codes and competition datasets on GitHub.
    4. M. Hanik, A. Demirtaş, M.A. Gharsallaoui and I. Rekik. Predicting cognitive scores with graph neural networks through sample selection learning. Journal of Brain Imaging and Behavior, IF: 3.39 (2021). Check out RegGNN GitHub code in Python.
    5. H. Bayram and I. Rekik.  A Federated Multigraph Itegration Approach for Connectional Brain Template Learning. In International Workshop on Multimodal Learning for Clinical Decision Support (2021) Springer, Cham. Check out our Fed-CBT code. arXiv Link.
    6. F. Pala, I. Mhiri and I. Rekik. Template-Based Inter-modality Super-resolution of Brain Connectivity. PRIME MICCAI workshop (2021), Strasbourg, France. (Oral presentation, Student Travel Award).  Check out the YouTube Video on TIS-Net, the video demo and our TIS-Net code.
    7. G. Ozen, A. Nebli and I. Rekik. FLAT-Net: Longitudinal Brain Graph Evolution Prediction from a Few Training Representative Templates. PRIME MICCAI workshop (2021), Strasbourg, France. (Oral presentation, Student Travel Award).  Check out the YouTube Video on FLAT-Net, the video demo and our FLAT-Net code.
    8. I. Mhiri, M. Mahjoub and I. Rekik. StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain Graph Alignment and Synthesis. Machine Learning in Medical Imaging (MLMI) MICCAI workshop (Student Travel Award) (2021), Strasbourg, France. Check out our SG-Net GitHub code, and YouTube Video.
    9. M.A Gharsallaoui, F. Torcani and I. Rekik. Investigating and Quantifying the Reproducibility of Graph Neural Networks in Predictive Medicine. PRIME MICCAI workshop (2021), Strasbourg, France. (Oral presentation, Student Travel Award). Check out our GitHub code on GNN reproducibility and the YouTube Video.
    10. B. Demir, A. Bessadok and I. Rekik. Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning.  Affordable Healthcare and AI for Resource Diverse Global Health (FAIR) MICCAI workshop (2021), Strasbourg, France, (Oral presentation, Student Travel Award).  Check out our L2S-KDNet GitHub code , the YouTube Video on L2S-KDNet, the video demo. Ranked 1st in the FAIR Presentation Runner-Up.
    11. U Guvercin, M.A Gharsallaoui and I. Rekik. One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction. PRIME MICCAI workshop (2021), Strasbourg, France. (Oral presentation). Check out our GitHub repo.
    12. A. Tekin, A. Nebli and I. Rekik. Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory. Affordable Healthcare and AI for Resource Diverse Global Health (FAIR) MICCAI workshop (2021), Strasbourg, France, (Oral presentation). Check out our RBGM GitHub code, the YouTube Video on RBGM, the video code demo.
    13. A. Bessadok, A. Nebli, M.A. Mahjoub, G. Li, W. Lin, D. Shen and I. Rekik. A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint. PRIME MICCAI workshop (2021), Strasbourg, France, (Oral presentation, Student Travel Award). Check out our GmTE-Net code, and YouTube video presentation.
    14. Bessadok, A., Mahjoub, M. A. and I. Rekik. Graph Neural Networks in Network Neuroscience, (2021). arXiv preprint arXiv:2106.03535. GitHub repo.
    15. O. Demirbilek and I. Rekik. Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates (MICCAI)  2021, Strasbourg, France (acceptance rate ~30%, MICCAI Student Travel Award).   Check out ReMI-Net GitHub code in Python. arXiv link.
    16. A. Bessadok, M.A. Mahjoub, and I. Rekik. Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network, Journal of Medical Image Analysis, IF: 13.82 (2021). Check out topoGAN GitHub code in Python.
    17. M. Isallari and I. Rekik. Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain Connectivity, Journal of Medical Image Analysis, IF: 13.82 (2021). Check out AGSR-Net GitHub code in Python.
    18. F Zhao, Z Chen, I. Rekik, P Liu, N Mao, S Lee and D Shen. A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks, Frontiers in Neuroscience  IF: 3.2 (2021).
    19. M.B. Gurbuz and I. Rekik. MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations, Journal of Medical Image Analysis, IF: 13.82 (2021). Check out MGN-Net GitHub code in Python.
    20. I. Mhiri, A. Nebli, M.A Mahjoub and I. Rekik. Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping, Information Processing in Medical Imaging (IPMI). Oral Presentation, 2021. Check out IMANGraphNet GitHub code in Python.
    21. M.D. Schirmer, A. Venkataraman, I. Rekik, M Kim, S.H. Mostofsky, M.B Nebel, K. et al. Neuropsychiatric Disease Classification Using Functional Connectomics – Results of the Connectomics in NeuroImaging Transfer Learning Challenge, Journal of Medical Image Analysis, IF: 13.82 (2021).

2020 (25 + 1 arXiv)

    1. A. Yalcin and I. Rekik. A Diagnostic Unified Classification Model for Classifying Multi-Sized and Multi-Modal Brain Graphs Using Graph Alignment, Journal of Neuroscience Methods, IF: 2.93 (2020). Check out UMC GitHub code in Python. Check out the YouTube Video on UMC, the video demo and our UMC code.
    2. A. Bessadok, M.A. Mahjoub, and I. Rekik. Brain Graph Synthesis by Dual Adversarial Domain Alignment and Target Graph Prediction from a Source, Journal of Medical Image Analysis, IF: 11.28 (2020).
    3. A. Sserwadda and I. Rekik. Topology-Guided Cyclic Brain Connectivity Generation using Geometric Deep Learning. Journal of Neuroscience Methods, IF: 2.93 (2020).
    4. M. Isallari  and I. Rekik. GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes. MLMI MICCAI workshop (2020), Lima, Peru. (Oral presentation).  Check out the YouTube Video on GSR-Net, the video demo and our GSR-Net code.
    5. M. Saglam and I. Rekik. Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State Profiling. GRAIL MICCAI (2020), Lima, Peru. (Oral presentation).  Check out the paper YouTube Video.
    6. U. Demir, M.A. Gharsallaoui  and I. Rekik. Clustering-based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates. GRAIL MICCAI (2020), Lima, Peru. Check out our cMGINet GitHub code.
    7. Z. Gurler, A. Nebli and I. Rekik. Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer. PRIME MICCAI workshop (2020), Lima, Peru. (Oral presentation).  Check out the YouTube Video on gGAN, the video demo and our gGAN code.
    8. A. Banka, I. Buzi and I. Rekik. Multi-View Brain HyperConnectome AutoEncoder For Brain State Classification. PRIME MICCAI workshop (2020), Lima, Peru. (Oral presentation).  Check out the YouTube Video on HCAE and our GitHub code.
    9. A.S. Goktas, A. Bessadok and I. Rekik. Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observation. PRIME MICCAI workshop (2020), Lima, Peru. (Oral presentation, Best Paper Award).  Check out the YouTube Video on RESNets, the video demo and our GitHub code.
    10. N. Bnouni, I. Rekik, M.S. Rhim, N. Essoukri Ben Amara. Context-Aware Synergetic Multiplex Network for Multi-Organ Segmentation of Cervical Cancer MRI. PRIME MICCAI workshop (2020), Lima, Peru. Check out the YouTube Video (Oral presentation). 
    11. A. Nebli, U.A. Kaplan and I. Rekik. Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint. PRIME MICCAI workshop (2020), Lima, Peru. (Oral presentation, Best Paper Award).  Check out EvoGraphNet GitHub code.
    12. A. Nebli and I. Rekik. Adversarial Brain Multiplex Prediction from a Single Network for High-Order Connectional Gender-Specific Brain Mapping. PRIME MICCAI workshop (2020), Lima, Peru. (Oral presentation).  Check out the YouTube Video and our GitHub code.
    13. O. Ghribi, G. Li, W Lin, D. Shen and I. Rekik. Multi-Regression Based Supervised Sample Selection for Predicting Baby Connectome Evolution Trajectory from Neonatal Timepoint, Journal of Medical Image Analysis, IF: 11.28 (2020).
    14. N. Chaari, H. Camgöz Akdağ and I. Rekik. Estimation of Gender-Specific Connectional Brain Templates using Joint Multi-View Cortical Morphological Network Integration, Journal of Brain Imaging and Behavior, IF: 3.39 (2020).
    15. M. Lostar and I. Rekik. Deep Hypergraph U-Net for Brain Graph Embedding and Classification. arXiv preprint arXiv:2008.13118 (2020).
    16. A. Nebli and I. Rekik. Adversarial Brain Multiplex Prediction From a Single Brain Network with Application to Gender Fingerprinting, Journal of Medical Image Analysis, IF: 11.28 (2020). Check out ABMT GitHub code.
    17. I. Mhiri, A. Ben Khalifa, M.A Mahjoub and I. Rekik. Brain Graph Super-Resolution for Boosting Neurological Disorder Diagnosis using Unsupervised Multi-Topology Residual Graph Manifold Learning, Journal of Medical Image Analysis, IF: 11.28 (2020). Check out BGSR GitHub code in Matlab or Python.
    18. M. Soussia, X. Wen, B. Jin, T.E. Kam, L.M Hsu, Z. Wu, G. Li, L. Wang,I. Rekik, W. Lin, D. Shen, H Zhang, and the UNC/UMN Baby Connectome Project Consortium. A Computational Framework for Dissociating Development-related from Individually Variable Flexibility in Regional Modularity Assignment in Early Infancy. MICCAI (2020), Lima, Peru (acceptance rate ~30%).
    19. M.B Gurbuz and I. Rekik. Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates. MICCAI (2020), Lima, Peru (early acceptance rate ~5%). Check out DGN GitHub code in Python, YouTube illustration of  the paper, and YouTube demo of the GitHub code.
    20. A. Bessadok, M.A. Mahjoub, and I. Rekik. Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph. MICCAI (2020), Lima, Peru (acceptance rate ~30%, MICCAI Student Travel Award). Check out MultiGraphGAN GitHub code in Python, YouTube illustration of  the paper, and YouTube demo of the GitHub code.
    21. I. Mhiri, M.A. Mahjoub, and I. Rekik. Supervised Multi-topology Network Cross-diffusion for Population-Driven Brain Network Atlas Estimation. MICCAI (2020), Lima, Peru (acceptance rate ~30%, MICCAI Student Travel Award). Check out SM-netFusion GitHub code in Matlab and Python, YouTube illustration of  the paper, and YouTube demo of the GitHub code.
    22. O.M. Benkarim, G. Piella, I. Rekik, N. Hahner, E. Eixarch, D. Shen, G. Li, M.A. Gonzalez Ballester, G. Sanroma. A novel approach to multiple anatomical shape analysis: Application to fetal ventriculomegaly. Journal of Medical Image Analysis, IF: 11.28 (2020).
    23. I. Bilgen, G. Guvercin, I. Rekik, Machine Learning Methods for Brain Network Classification: Application to Autism Diagnosis using Cortical Morphological Networks. Journal of Neuroscience Methods, IF: 2.93 (2020).
    24. F. Zhao, Z. Chen, I. Rekik, D. Shen. Diagnosis of autism spectrum disorder using central-moment features from low-and-high-order dynamic resting-state functional connectivity networks. Frontiers in Neuroscience, IF: 3.64 (2020).
    25. G. Chen, Q. Li, F. Shi, I. Rekik, Z. Pan. RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields. Journal of Neuroimage, IF: 5.81 (2020).
    26. M. Madine, I. Rekik, N. Werghi. Diagnosing Autism spectrum disorder using T1-MRI with multi-kernel learning and hypergraph neural network. ICIP (2020), Abu Dhabi, UAE.

2019 (19 + 1 arXiv)

    1. D. Duan, S. Xia, I. Rekik, Z. Wu, L. Wang, W. Lin, J.H Gilmore, D. Shen, G. Li. Individual Identification and Individual Variability Analysis Based on Cortical Folding Features in Developing Infant Singletons and Twins, Human Brain Mapping, IF: 4.55 (2019).
    2. N. Georges, I. Mhiri and I. Rekik. Identifying the best data-driven feature selection method for boosting reproducibility in classification tasks, Journal of Pattern Recognition, IF: 7.19 (2019).
    3. I. Mhiri and I. Rekik. Joint Functional Brain Network Atlas Estimation and Feature Selection for Neurological Disorder Diagnosis With Application to Autism, Journal of Medical Image Analysis, IF: 8.88 (2019). (Check out NAG-FS GitHub code in Matlab and Python).
    4. S. Dhifallah and I. Rekik. Estimation of Connectional Brain Templates using Selective Multi-View Network Normalization, Journal of Medical Image Analysis, IF: 8.88 (2019). (Check out netNorm GitHub code in Matlab and Python).
    5. N. Bnouni, I. Rekik, M.S. Rhim, N. Essoukri Ben Amara. Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging. International Journal of Computing and Digital Systems, (2019).
    6. A. Banka and I. Rekik. Adversarial Connectome Embedding for Mild Cognitive Impairment Identification using Cortical Morphological Networks. CNI MICCAI workshop (2019), Shenzhen, China.
    7. O. Ghribi, G. Li, W. Lin, D. Shen. and I. Rekik. Progressive Infant Brain Connectivity Evolution Prediction from Neonatal MRI using Bidirectionally Supervised Sample Selection. PRIME MICCAI workshop (2019), Shenzhen, China. (Oral presentation)
    8. K. Cengiz and I. Rekik. Predicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-Resolution Manifolds, PRIME MICCAI workshop (2019), Shenzhen, China.
    9. O. Ben Khelifa and I. Rekik. Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia States, CNI MICCAI workshop (2019), Shenzhen, China.
    10. A. Bessadok, MA Mahjoub, and I. Rekik. Hierarchical Adversarial Connectomic Domain Alignment for Target Brain Graph Prediction and Classification From a Source Graph, PRIME MICCAI workshop (2019), Shenzhen, China.  (Oral presentation, check out the YouTube Video and HADA GitHub code and demo!)
    11. M. Soussia and I. Rekik. 7 years of Developing Seed Techniques for Alzheimer’s Disease Diagnosis using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis, PRIME MICCAI workshop (2019), Shenzhen, China.
    12. C. Gafuroglu and I. Rekik. Image Evolution Trajectory Prediction and Classification from Baseline using Learning-based Patch Atlas Selection for Early Diagnosis, arXiv (2019).
    13. O. Graa and I. Rekik. Multi-View Learning-Based Data Proliferator for Boosting Classification Using Highly Imbalanced Classes, Journal of Neuroscience Methods, (2019). (Check out MV-LEAP GitHub code).
    14. A. Bessadok, MA Mahjoub, and I. Rekik. Symmetric Dual Adversarial Connectomic Domain Alignment for Predicting Isomorphic Brain Graph From a Baseline Graph. MICCAI (2019), Shenzhen, China (acceptance rate 31%, MICCAI Young Student Award).
    15. BE Ezzine and I. Rekik. Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation. MICCAI (2019), Shenzhen, China (acceptance rate 31%).
    16. J. Corps, and I. Rekik. Morphological brain age prediction using multi-view brain networks derived from cortical morphology in healthy and disordered participants, Nature Scientific Reports, (2019).
    17. A. Nebli, and I. Rekik. Gender differences in cortical morphological networks, Brain Imaging and Behavior, (2019).
    18. E. Dryburgh, S. McKenna, I. Rekik. Predicting Full-Scale and Verbal Intelligence Scores from Functional Connectomic Data in Individuals with Autism Spectrum Disorder, Brain Imaging and Behavior, (2019).
    19. N. Bnouni, I. Rekik, M.S. Rhim, N. Essoukri Ben Amara. Cross-View Self-Similarity Using Shared Dictionary Learning for Cervical Cancer Staging. IEEE Access, (2019).
    20. Z. Feng, I. Rekik, J. Liu, D. Shen. Two-phase incremental kernel PCA for learning massive or online datasets. Complexity, (2019).

2018 (27 + 1 arXiv)

    1. L. Fang, L. Zhang, D. Nie, X. Cao, I. Rekik, S-W Lee, H He, D Shen. Automatic Brain Labeling via Multi-Atlas Guided Fully Convolutional Networks.  Medical Image Analysis, (2018).
    2. M. Soussia and I. Rekik. Unsupervised Manifold Learning using High-order Morphological Brain Networks derived from T1-w MRI for Autism Diagnosis. Frontiers in Neuroinformatics, (2018).
    3. S. Dhifallah and I. Rekik. Clustering-based Multi-View Network Fusion for Estimating Brain Network Atlases of Healthy and Disordered Populations. Journal of Neuroscience Methods, (2018).
    4. D. Duan, S. Xia, I. Rekik, Y. Meng, Z. Wu, L. Wang, W. Lin, J.H Gilmore, D. Shen, G. Li. Exploring Folding Patterns of Infant Cerebral Cortex Based on Multi-view Curvature Features: Methods and Applications, Neuroimage, (2018).
    5. L. Zhang, H. Zhang, I. Rekik, Y. Gao, Q. Wang, D. Shen. Malignant Brain Tumor Classification Using the Random Forest Method. Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) (2018).
    6. M. Soussia and I. Rekik. A Review on Image-and Network-based Brain Data Analysis Techniques for Alzheimer’s Disease Diagnosis Reveals a Gap in Developing Predictive Methods for Prognosis. arXiv (2018).
    7. L. Liu, H. Zhang, J. Wu, Z. Yu, X. Chen, I. Rekik, Q. Wang, J. Lu, D. Shen. Overall Survival Time Prediction for High-grade Glioma Patients based on Large-scale Brain Functional Networks. Brain Imaging and Behavior (2018).
    8. R. Raeper, A. Lisowska, I. Rekik. Cooperative Correlational and Discriminative Ensemble Classifier Learning for Early Dementia Diagnosis Using Morphological Brain Multiplexes. IEEE Access (2018).
    9. N. Bnouni, I. Rekik, M.S. Rhim, N. Essoukri Ben Amara. Dynamic Multi-Scale CNN Forest Learning for Automatic Cervical Cancer Segmentation. MICCAI MLMI (Machine Learning in Medical Imaging) workshop (2018), Granada, Spain.
    10. N. Georges, I. Rekik. Data-Specific Feature Selection Method Identification for Most Reproducible Connectomic Feature Discovery Fingerprinting Brain States.MICCAI CNI (Connectomics in NeuroImaging) workshop (2018), Granada, Spain.
    11. A. Bessadok, I. Rekik. Intact Connectional Morphometricity Learning using Multi-View Morphological Brain Networks with Application to Autism Spectrum Disorder. MICCAI CNI (Connectomics in NeuroImaging) workshop (2018), Granada, Spain.
    12. S. Bano, M. Asad, A.E Fetit, I. Rekik. XmoNet: a Fully Convolutional Network for Cross-Modality MR Image Inference. MICCAI PRIME (PRedictive Intelligence in Medicine) workshop (2018), Granada, Spain.
    13. M. Zhu, I. Rekik. Multi-View Brain Network Prediction From a Source View Using Sample Selection via CCA-based Multi-Kernel Connectomic Manifold Learning. MICCAI PRIME (PRedictive Intelligence in Medicine) workshop (2018), Granada, Spain.Granada, Spain. (Oral presentation)
    14. A. Lisowska, I. Rekik. Predicting Emotional Intelligence Scores From Multi-Session Functional Brain Connectomes. MICCAI PRIME (PRedictive Intelligence in Medicine) workshop (2018), Granada, Spain.
    15. A. Lisowska, I. Rekik. Joint Pairing and Structured Mapping of Convolutional Brain Morphological Multiplexes for Early Dementia Diagnosis, Brain Connectivity, (2018).
    16. S. Amiri, M.A. Mahjoub, I. Rekik. Tree-based Ensemble Classifier Learning for Automatic Brain Glioma Segmentation. Neurocomputing, (2018).
    17. I. Rekik, G. Li, W. Lin, D. Shen. Do Baby Brain Cortices that Look Alike at Birth Grow Alike During The First Year of Postnatal Development?. MICCAI (2018), Granada, Spain. Supplementary Material Project
    18. C. Gafuroglu, I. Rekik. Joint Prediction and Classification of Brain Image Evolution Trajectories from Baseline Brain Image with Application to Early Dementia. MICCAI (2018), Granada, Spain.
    19. R. Raeper, A. Lisowska, I. Rekik. Joint Correlational and Discriminative Ensemble Classifier Learning for Dementia Stratification Using Shallow Brain Multiplexes. MICCAI (2018), Granada, Spain.
    20. O.M. Benkarim, G Sanroma, G. Piella, I. Rekik, N. Hahner, E. Eixarch, MA. Gonzalez Ballester, D Shen, Gang Li. Revealing Regional Associations of Cortical Folding Alterations with In Utero Ventricular Dilation Using Joint Spectral Embedding. MICCAI (2018), Granada, Spain.
    21. F. Zhao, H. Zhang, I. Rekik, Z. An, D. Shen. Diagnosis of Autism Spectrum Disorders Using Multi-level High-order Functional Networks Derived from Resting-State Functional MRI. Frontiers in Human Neuroscience (2018).
    22. N. Bnouni, H. Ben Amor, I. Rekik, M. Salah Rhim, B. Solaiman, N Essoukri Ben Amara. Boosting CNN Learning by Ensemble Image Preprocessing Methods for Cervical Cancer MR Image Segmentation. SSS (2018).
    23. G. Li, L. Wang,P.T.  Yap, F. Wang, Z. Wu, Y. Meng, P. Dong, J. Kim, F. Shi, I. Rekik, W. Lin, D. Shen. Computational neuroanatomy of baby brains: A review. NeuroImage, (2018).
    24. I. Mahjoub, M.A. Mahjoub, I. Rekik. Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Scientific reports, 8(1), p.4103.
    25. N. Bnouni, O. Mechi, I. Rekik, M.S. Rhim, N. Essoukri Ben Amara. Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging. ATSIP (2018). (Oral presentation, Best Paper Award)
    26. I. Rekik, G. Li, W. Lin, D. Shen. Estimation of shape and growth brain network atlases for connectomic brain mapping in developing infants, ISBI (2018).
    27. S. Amiri, M.A. Mahjoub, I. Rekik. Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions. VISAPP (2018).
    28. S. Amiri, M.A. Mahjoub, I. Rekik. Bayesian Network and Structured Random Forest Cooperative Deep Learning For Automatic Multi-label Brain Tumor Segmentation. ICAART (2018).

2017 (10)

    1. A. Lisowska, I. Rekik. Pairing-based Ensemble Classifier Learning using Convolutional Brain Multiplexes & Multi-view Brain Networks for Early Dementia Diagnosis. MICCAI, CNI workshop, (2017).
    2. C. Morris, I. Rekik. Autism Spectrum Disorder Diagnosis Using Sparse Graph Embedding of Morphological Brain Networks. MICCAI, GRAIL workshop, (2017). (Oral presentation).
    3. M. Soussia, I. Rekik. High-order Connectomic Manifold Learning for Autistic Brain State Identification. 
MICCAI, CNI workshop, (2017). (Oral presentation, Best Paper Award)
    4. K. Bahrami, I. Rekik, F. Shi, D. Shen. Joint Reconstruction-Segmentation of 7T-like MR images from 3T 
MRI based on Cascaded Convolutional Neural Networks. MICCAI (2017).
    5. H. Wen, Y. Liu, I. Rekik, S. Wang, J. Zhang, Y. Zhang, Y. Peng, H. He. Abnormal Topological Organization of Structural Networks revealed by Probabilistic Diffusion Tractography in Tourette Syndrome Children. Human Brain Mapping (2017).
    6. K. Bahrami, F. Shi, I. Rekik, Y. Gao, D. Shen. 7T‐Guided Super‐Resolution of 3T MRI. Medical Physics (2017).
    7. D. Duan, I. Rekik, S. Xia, W. Lin, J.H Gilmore, D. Shen, G. Li. Longitudinal Multi-Scale Mapping of Infant Cortical Folding using Spherical Wavelets. IEEE International Symposium on Biomedical Imaging (ISBI), 2017.
    8. I. Rekik, G. Li, W. Lin, D. Shen. Estimation of Brain Network Atlases using Diffusive-Shrinking Graphs: Application to Developing Brains. Information Processing in Medical Imaging (IPMI), (2017, acceptance rate ~25%). PDF Project
    9. I. Rekik, G. Li, P-T Yap, G. Chen, W. Lin, D. Shen. Joint Prediction of Longitudinal Development of Cortical Surfaces and White Matter Fibers from Neonatal MRI. Neuroimage (2017).
    10. Y. Meng, G. Li, I. Rekik, W. Lin, D. Shen. Can we predict subject-specific dynamic cortical thickness maps during infancy from birth? Human Brain Mapping (2017).

2016 (10)

    1. L. Liu, H. Zhang, I. Rekik, Q. Wang, D. Shen. Outcome Prediction for Patient with High-grade Gliomas from Brain Functional and Structural Networks. MICCAI (2016) (Oral presentation, acceptance rate ~5%).
    2. M. Kim, G. Wu, I. Rekik, D. Shen. Dual-layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images. MICCAI, MLMI workshop (2016).
    3. K. Bahrami, F. Shi, I. Rekik, D. Shen. Convolutional Neural Network for Reconstruction of 7T MRI from 3T MRI Using Appearance and Anatomical Features. MICCAI, DLMI workshop (2016) (Oral presentation).
    4. K. Bahrami, I. Rekik, F. Shi, Y. Gao, D. Shen. 7T-Guided Learning Framework for Improving the Segmentation of 3T MR Images. MICCAI (2016).
    5. I. Rekik, G. Li, P.T Yap, G. Chen, W. Lin, D. Shen. A Hybrid Multishape Learning Framework for Longitudinal Prediction of Cortical Surfaces and Fiber Tracts Using Neonatal Data. MICCAI (2016).
    6. S. Amiri, I. Rekik, M.A. Mahjoub. Deep Random Forest-based Learning Transfer to SVM for Brain Tumor Segmentation. ATSIP (2016).
    7. H. Wen, Y. Liu, J. Wang, I. Rekik, J. Zhang, Y. Zhang, Y. Peng, H. He. Combining tract- and atlas-based analysis reveals micro-structural abnormalities in Early Tourette Syndrome Children. Human Brain Mapping (2016).
    8. H. Wen, Y. Liu, I. Rekik; S. Wang, Z. Chen, J. Zhang, Y. Peng. Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children. Pattern Recognition (2016).
    9. I. Rekik, G. Li, W. Lin, D. Shen. Multidirectional and Topography-based Dynamic-scale Varifold Representations with Application to Matching Developing Cortical Surfaces. Neuroimage (2016).
    10. I. Rekik, G. Li, W. Lin, D. Shen. Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing. Medical Image Analysis (2016).

2015 (4)

    1. I. Rekik, G. Li, W. Lin, D. Shen. Prediction of infant MRI appearance and anatomical structure evolution using sparse patch-based metamorphosis learning framework. MICCAI, Patch-MI workshop (2015).
    2. I. Rekik, G. Li, W. Lin, D. Shen. Topography-based registration of developing cortical surfaces in infants using multidirectional varifold representation. MICCAI (2015). (Oral presentation, acceptance rate ~5%).
    3. I. Rekik, G. Li, W. Lin, D. Shen. Prediction of longitudinal development of infant cortical surface shape using a 4d current-based learning framework. Information Processing in Medical Imaging (2015, acceptance rate ~25%).
    4. I. Rekik, S. Allassonnière, M. Luby, T. Carpenter, J. Wardlaw. Phase-based metamorphosis of diffusion lesion in relation to perfusion values in acute ischemic stroke. NeuroImage: Clinical (2015).

2014 (1)

    1. I. Rekik, S. Allassonnière, T. Carpenter, J. Wardlaw. Using longitudinal metamorphosis to examine ischemic stroke lesion dynamics on perfusion-weighted images and in relation to final outcome on T2-w images. NeuroImage: Clinical (2014).

2013 (3)

      A. I. Rekik, S. Allassonnière, S. Durrleman, T. Carpenter, J. Wardlaw. Spatio-temporal dynamic simulation of acute perfusion/diffusion ischemic stroke lesions: a pilot study derived from longitudinal MR patient data. Computational and Mathematical Methods in Medicine (2013).

      B. I. Rekik, S. Allassonnière, O. Clatz, E. Geremia, E. Stretton, H. Delingette, N. Ayache. Tumor Growth Parameters Estimation and Source Localization From a Unique Time Point: Application to Low-grade Gliomas. Computer Vision and Image Understanding (2013).

      C. I. Rekik, S. Allassonnière, T. Carpenter, J. Wardlaw. Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation. NeuroImage: Clinical (2012).

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