​    ​2022 (3)

  1. 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 NetworksIF: 8.05 (2022).
  2. 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).
  3. 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 SegmentationNeuroimage, 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 NetworksIF: 8.05 (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 MethodsIF: 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 BehaviorIF: 3.39 (2021). Check out RegGNN GitHub code in Python.
  5. H. Bayram and I. RekikA 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 ConnectivityPRIME 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 PredictionPRIME 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 NetworkJournal of Medical Image AnalysisIF: 11.28 (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 ConnectivityJournal of Medical Image AnalysisIF: 11.28 (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 NetworksFrontiers 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 populationsJournal of Medical Image AnalysisIF: 11.28 (2021). Check out MGN-Net GitHub code in Python.
  20. I. Mhiri, A. Nebli, M.A Mahjoub and I. RekikNon-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 alNeuropsychiatric Disease Classification Using Functional Connectomics - Results of the Connectomics in NeuroImaging Transfer Learning ChallengeJournal of Medical Image AnalysisIF: 11.28 (2021).

    2020 (25 1 arXiv)

  22. A. Yalcin and I. Rekik. A Diagnostic Unified Classification Model for Classifying Multi-Sized and Multi-Modal Brain Graphs Using Graph AlignmentJournal of Neuroscience MethodsIF: 2.93 (2020). Check out UMC GitHub code in Python. Check out the YouTube Video on UMC, the video demo and our UMC code.
  23. A. Bessadok, M.A. Mahjoub, and I. Rekik. Brain Graph Synthesis by Dual Adversarial Domain Alignment and Target Graph Prediction from a SourceJournal of Medical Image AnalysisIF: 11.28 (2020).
  24. A. Sserwadda and I. Rekik. Topology-Guided Cyclic Brain Connectivity Generation using Geometric Deep LearningJournal of Neuroscience MethodsIF: 2.93 (2020).
  25. M. Isallari  and I. Rekik. GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain ConnectomesMLMI MICCAI workshop (2020), Lima, Peru. (Oral presentation).  Check out the YouTube Video on GSR-Net, the video demo and our GSR-Net code.
  26. M. Saglam and I. Rekik. Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State ProfilingGRAIL MICCAI (2020), Lima, Peru. (Oral presentation).  Check out the paper YouTube Video.
  27. U. Demir, M.A. Gharsallaoui  and I. Rekik. Clustering-based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain TemplatesGRAIL MICCAI (2020), Lima, Peru. Check out our cMGINet GitHub code.
  28. Z. Gurler, A. Nebli and I. Rekik. Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network NormalizerPRIME MICCAI workshop (2020), Lima, Peru. (Oral presentation).  Check out the YouTube Video on gGAN, the video demo and our gGAN code.
  29. A. Banka, I. Buzi and I. Rekik. Multi-View Brain HyperConnectome AutoEncoder For Brain State ClassificationPRIME MICCAI workshop (2020), Lima, Peru. (Oral presentation).  Check out the YouTube Video on HCAE and our GitHub code.
  30. A.S. Goktas, A. Bessadok and I. Rekik. Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single ObservationPRIME MICCAI workshop (2020), Lima, Peru. (Oral presentation, Best Paper Award).  Check out the YouTube Video on RESNets, the video demo and our GitHub code.
  31. N. Bnouni, I. Rekik, M.S. Rhim, N. Essoukri Ben Amara. Context-Aware Synergetic Multiplex Network for Multi-Organ Segmentation of Cervical Cancer MRIPRIME MICCAI workshop (2020), Lima, Peru. Check out the YouTube Video (Oral presentation). 
  32. A. Nebli, U.A. Kaplan and I. Rekik. Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single TimepointPRIME MICCAI workshop (2020), Lima, Peru. (Oral presentation, Best Paper Award).  Check out EvoGraphNet GitHub code.
  33. A. Nebli and I. Rekik. Adversarial Brain Multiplex Prediction from a Single Network for High-Order Connectional Gender-Specific Brain MappingPRIME MICCAI workshop (2020), Lima, Peru. (Oral presentation).  Check out the YouTube Video and our GitHub code.
  34. 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 TimepointJournal of Medical Image AnalysisIF: 11.28 (2020).
  35. N. Chaari, H. Camgöz Akdağ and I. Rekik. Estimation of Gender-Specific Connectional Brain Templates using Joint Multi-View Cortical Morphological Network IntegrationJournal of Brain Imaging and BehaviorIF: 3.39 (2020).
  36. M. Lostar and I. RekikDeep Hypergraph U-Net for Brain Graph Embedding and ClassificationarXiv preprint arXiv:2008.13118 (2020).
  37. A. Nebli and I. Rekik. Adversarial Brain Multiplex Prediction From a Single Brain Network with Application to Gender FingerprintingJournal of Medical Image AnalysisIF: 11.28 (2020). Check out ABMT GitHub code.
  38. I. Mhiri, A. Ben Khalifa, M.A Mahjoub and I. RekikBrain Graph Super-Resolution for Boosting Neurological Disorder Diagnosis using Unsupervised Multi-Topology Residual Graph Manifold LearningJournal of Medical Image AnalysisIF: 11.28 (2020). Check out BGSR GitHub code in Matlab or Python.
  39. 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%).
  40. 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 PythonYouTube illustration of  the paper, and YouTube demo of the GitHub code.
  41. 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 PythonYouTube illustration of  the paper, and YouTube demo of the GitHub code.
  42. 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 PythonYouTube illustration of  the paper, and YouTube demo of the GitHub code.
  43. 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 ventriculomegalyJournal of Medical Image AnalysisIF: 11.28 (2020).
  44. I. Bilgen, G. Guvercin, I. Rekik, Machine Learning Methods for Brain Network Classification: Application to Autism Diagnosis using Cortical Morphological Networks. Journal of Neuroscience MethodsIF: 2.93 (2020).
  45. 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 networksFrontiers in NeuroscienceIF: 3.64 (2020).
  46. G. Chen, Q. Li, F. Shi, I. Rekik, Z. Pan. RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fieldsJournal of NeuroimageIF: 5.81 (2020).
  47. 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)

  48. 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 TwinsHuman Brain Mapping, IF: 4.55 (2019).
  49. N. Georges, I. Mhiri and I. Rekik. Identifying the best data-driven feature selection method for boosting reproducibility in classification tasksJournal of Pattern RecognitionIF: 7.19 (2019).
  50. I. Mhiri and I. Rekik. Joint Functional Brain Network Atlas Estimation and Feature Selection for Neurological Disorder Diagnosis With Application to AutismJournal of Medical Image AnalysisIF: 8.88 (2019). (Check out NAG-FS GitHub code in Matlab and Python).
  51. S. Dhifallah and I. Rekik. Estimation of Connectional Brain Templates using Selective Multi-View Network NormalizationJournal of Medical Image Analysis, IF: 8.88 (2019). (Check out netNorm GitHub code in Matlab and Python).
  52. N. Bnouni, I. Rekik, M.S. Rhim, N. Essoukri Ben Amara. Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance ImagingInternational Journal of Computing and Digital Systems(2019).
  53. A. Banka and I. RekikAdversarial Connectome Embedding for Mild Cognitive Impairment Identification using Cortical Morphological NetworksCNI MICCAI workshop (2019), Shenzhen, China.
  54. O. Ghribi, G. Li, W. Lin, D. Shen. and I. RekikProgressive Infant Brain Connectivity Evolution Prediction from Neonatal MRI using Bidirectionally Supervised Sample SelectionPRIME MICCAI workshop (2019), Shenzhen, China. (Oral presentation)
  55. K. Cengiz and I. RekikPredicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-Resolution ManifoldsPRIME MICCAI workshop (2019), Shenzhen, China.
  56. O. Ben Khelifa and I. Rekik. Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia StatesCNI MICCAI workshop (2019), Shenzhen, China.
  57. A. Bessadok, MA Mahjoub, and I. Rekik. Hierarchical Adversarial Connectomic Domain Alignment for Target Brain Graph Prediction and Classification From a Source GraphPRIME MICCAI workshop (2019), Shenzhen, China.  (Oral presentation, check out the YouTube Video and HADA GitHub code and demo!)
  58. 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 PrognosisPRIME MICCAI workshop (2019), Shenzhen, China.
  59. C. Gafuroglu and I. Rekik. Image Evolution Trajectory Prediction and Classification from Baseline using Learning-based Patch Atlas Selection for Early DiagnosisarXiv (2019).
  60. O. Graa and I. RekikMulti-View Learning-Based Data Proliferator for Boosting Classification Using Highly Imbalanced ClassesJournal of Neuroscience Methods, (2019). (Check out MV-LEAP GitHub code).
  61. 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).
  62. 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%).
  63. 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).
  64. A. Nebli, and I. Rekik. Gender differences in cortical morphological networksBrain Imaging and Behavior, (2019).
  65. E. Dryburgh, S. McKenna, I. RekikPredicting Full-Scale and Verbal Intelligence Scores from Functional Connectomic Data in Individuals with Autism Spectrum DisorderBrain Imaging and Behavior, (2019).
  66. N. Bnouni, I. Rekik, M.S. Rhim, N. Essoukri Ben Amara. Cross-View Self-Similarity Using Shared Dictionary Learning for Cervical Cancer StagingIEEE Access, (2019).
  67. 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)

  68. 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).
  69. 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).
  70. S. Dhifallah and I. RekikClustering-based Multi-View Network Fusion for Estimating Brain Network Atlases of Healthy and Disordered PopulationsJournal of Neuroscience Methods, (2018).
  71. 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).
  72. 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).
  73. 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).
  74. 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).
  75. R. Raeper, A. Lisowska, I. Rekik. Cooperative Correlational and Discriminative Ensemble Classifier Learning for Early Dementia Diagnosis Using Morphological Brain Multiplexes. IEEE Access (2018).
  76. 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.
  77. 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.
  78. 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.
  79. 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.
  80. 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)
  81. A. Lisowska, I. Rekik. Predicting Emotional Intelligence Scores From Multi-Session Functional Brain Connectomes. MICCAI PRIME (PRedictive Intelligence in Medicine) workshop (2018), Granada, Spain.
  82. A. Lisowska, I. Rekik. Joint Pairing and Structured Mapping of Convolutional Brain Morphological Multiplexes for Early Dementia Diagnosis, Brain Connectivity, (2018).
  83. S. Amiri, M.A. Mahjoub, I. Rekik. Tree-based Ensemble Classifier Learning for Automatic Brain Glioma Segmentation. Neurocomputing, (2018).
  84. 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
  85. 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.
  86. R. Raeper, A. Lisowska, I. Rekik. Joint Correlational and Discriminative Ensemble Classifier Learning for Dementia Stratification Using Shallow Brain Multiplexes. MICCAI (2018), Granada, Spain.
  87. 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.

  88. 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).
  89. 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).
  90. 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).
  91. I. Mahjoub, M.A. Mahjoub, I. Rekik. Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Scientific reports, 8(1), p.4103.
  92. 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)
  93. 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).
  94. 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).
  95. 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)

  96. 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).
  97. C. Morris, I. Rekik. Autism Spectrum Disorder Diagnosis Using Sparse Graph Embedding of Morphological Brain Networks. MICCAI, GRAIL workshop, (2017). (Oral presentation).
  98. M. Soussia, I. Rekik. High-order Connectomic Manifold Learning for Autistic Brain State Identification. 
MICCAI, CNI workshop, (2017). (Oral presentation, Best Paper Award)
  99. 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).
  100. 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).
  101. K. Bahrami, F. Shi, I. Rekik, Y. Gao, D. Shen. 7T‐Guided Super‐Resolution of 3T MRI. Medical Physics (2017).
  102. 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.
  103. 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
  104. 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).
  105. 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)

  106. 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%).
  107. M. Kim, G. Wu, I. Rekik, D. Shen. Dual-layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images. MICCAI, MLMI workshop (2016).
  108. 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).
  109. K. Bahrami, I. Rekik, F. Shi, Y. Gao, D. Shen. 7T-Guided Learning Framework for Improving the Segmentation of 3T MR Images. MICCAI (2016).
  110. 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).
  111. S. Amiri, I. Rekik, M.A. Mahjoub. Deep Random Forest-based Learning Transfer to SVM for Brain Tumor Segmentation. ATSIP (2016).
  112. 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).
  113. 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).
  114. 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).
  115. 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)

  116. 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).
  117. 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%).
  118. 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%).
  119. 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)

  120. 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)

  121. 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).
  122. 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).
  123. 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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