2020 (11 1 arXiv)

  1. 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 AnalysisIF: 11.14 (2020) —in press.
  2. M. Lostar and I. RekikDeep Hypergraph U-Net for Brain Graph Embedding and ClassificationarXiv preprint arXiv:2008.13118 (2020).
  3. A. Nebli and I. Rekik. Adversarial Brain Multiplex Prediction From a Single Brain Network with Application to Gender Fingerprinting, Journal of Medical Image AnalysisIF: 11.14 (2020). Check out ABMT GitHub code —in press.
  4. 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: 11.14 (2020). Check out BGSR GitHub code in Matlab or Python.
  5. 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%).
  6. 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).
  7. 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.
  8. 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: 11.14 (2020).
  9. 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) —in press
  10. 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).
  11. 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).
  12. 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)

  13. 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).
  14. N. Georges, I. Mhiri and I. Rekik. Identifying the best data-driven feature selection method for boosting reproducibility in classification tasksJournal of Pattern RecognitionIF: 5.89 (2019).
  15. 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).
  16. 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).
  17. 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).
  18. A. Banka and I. RekikAdversarial Connectome Embedding for Mild Cognitive Impairment Identification using Cortical Morphological NetworksCNI MICCAI workshop (2019), Shenzhen, China.
  19. 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)
  20. K. Cengiz and I. RekikPredicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-Resolution ManifoldsPRIME MICCAI workshop (2019), Shenzhen, China.
  21. O. Ben Khelifa and I. Rekik. Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia StatesCNI MICCAI workshop (2019), Shenzhen, China.
  22. 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!)
  23. 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.
  24. C. Gafuroglu and I. Rekik. Image Evolution Trajectory Prediction and Classification from Baseline using Learning-based Patch Atlas Selection for Early DiagnosisarXiv (2019).
  25. 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).
  26. 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).
  27. 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%).
  28. 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).
  29. A. Nebli, and I. Rekik. Gender differences in cortical morphological networksBrain Imaging and Behavior, (2019).
  30. 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).
  31. 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).
  32. 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)

  33. 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).
  34. 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).
  35. S. Dhifallah and I. RekikClustering-based Multi-View Network Fusion for Estimating Brain Network Atlases of Healthy and Disordered PopulationsJournal of Neuroscience Methods, (2018).
  36. 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).
  37. 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).
  38. 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).
  39. 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).
  40. R. Raeper, A. Lisowska, I. Rekik. Cooperative Correlational and Discriminative Ensemble Classifier Learning for Early Dementia Diagnosis Using Morphological Brain Multiplexes. IEEE Access (2018) —in press.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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)
  46. A. Lisowska, I. Rekik. Predicting Emotional Intelligence Scores From Multi-Session Functional Brain Connectomes. MICCAI PRIME (PRedictive Intelligence in Medicine) workshop (2018), Granada, Spain.
  47. A. Lisowska, I. Rekik. Joint Pairing and Structured Mapping of Convolutional Brain Morphological Multiplexes for Early Dementia Diagnosis, Brain Connectivity, (2018).
  48. S. Amiri, M.A. Mahjoub, I. Rekik. Tree-based Ensemble Classifier Learning for Automatic Brain Glioma Segmentation. Neurocomputing, (2018).
  49. 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
  50. 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.
  51. R. Raeper, A. Lisowska, I. Rekik. Joint Correlational and Discriminative Ensemble Classifier Learning for Dementia Stratification Using Shallow Brain Multiplexes. MICCAI (2018), Granada, Spain.
  52. 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.

  53. 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).
  54. 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).
  55. 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).
  56. I. Mahjoub, M.A. Mahjoub, I. Rekik. Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Scientific reports, 8(1), p.4103.
  57. 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)
  58. 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).
  59. 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).
  60. 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)

  61. 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).
  62. C. Morris, I. Rekik. Autism Spectrum Disorder Diagnosis Using Sparse Graph Embedding of Morphological Brain Networks. MICCAI, GRAIL workshop, (2017). (Oral presentation).
  63. M. Soussia, I. Rekik. High-order Connectomic Manifold Learning for Autistic Brain State Identification. 
MICCAI, CNI workshop, (2017). (Oral presentation, Best Paper Award)
  64. 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).
  65. 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).
  66. K. Bahrami, F. Shi, I. Rekik, Y. Gao, D. Shen. 7T‐Guided Super‐Resolution of 3T MRI. Medical Physics (2017).
  67. 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.
  68. 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
  69. 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).
  70. 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)

  71. 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%).
  72. M. Kim, G. Wu, I. Rekik, D. Shen. Dual-layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images. MICCAI, MLMI workshop (2016).
  73. 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).
  74. K. Bahrami, I. Rekik, F. Shi, Y. Gao, D. Shen. 7T-Guided Learning Framework for Improving the Segmentation of 3T MR Images. MICCAI (2016).
  75. 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).
  76. S. Amiri, I. Rekik, M.A. Mahjoub. Deep Random Forest-based Learning Transfer to SVM for Brain Tumor Segmentation. ATSIP (2016).
  77. 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).
  78. 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).
  79. 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).
  80. 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)

  81. 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).
  82. 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%).
  83. 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%).
  84. 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)

  85. 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)

  86. 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).
  87. 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).
  88. 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).

Copyright notice: The available electronic documents are provided for personal use only. Copyright and all rights are retained by authors or by other copyright holders. These are not to be diffused or disseminated.

Close Menu