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  1. 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) —in press.
  2. 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).
  3. 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 Mar 21.
  4. I. Mahjoub, M.A. Mahjoub, I. Rekik. Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Scientific reports, 8(1), p.4103.
  5. 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)
  6. 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).
  7. 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 (2017).
  8. S. Amiri, M.A. Mahjoub, I. Rekik. Bayesian Network and Structured Random Forest Cooperative Deep Learning For Automatic Multi-label Brain Tumor Segmentation. ICAART (2017).


  9. 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).
  10. C. Morris, I. Rekik. Autism Spectrum Disorder Diagnosis Using Sparse Graph Embedding of Morphological Brain Networks. MICCAI, GRAIL workshop, (2017). (Oral presentation).
  11. M. Soussia, I. Rekik. High-order Connectomic Manifold Learning for Autistic Brain State Identification. 
MICCAI, CNI workshop, (2017). (Oral presentation, Best Paper Award)
  12. 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).
  13. 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).
  14. K. Bahrami, F. Shi, I. Rekik, Y. Gao, D. Shen. 7T‐Guided Super‐Resolution of 3T MRI. Medical Physics (2017).
  15. 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.
  16. 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
  17. 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).
  18. Y. Meng, G. Li, I. Rekik, W. Lin, S. Dinggang. Can we predict subject-specific dynamic cortical thickness maps during infancy from birth? Human Brain Mapping (2017).


  19. 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%).
  20. M. Kim, G. Wu, I. Rekik, D. Shen. Dual-layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images. MICCAI, MLMI workshop (2016).
  21. 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).
  22. K. Bahrami, I. Rekik, F. Shi, Y. Gao, D. Shen. 7T-Guided Learning Framework for Improving the Segmentation of 3T MR Images. MICCAI (2016).
  23. 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).
  24. S. Amiri, I. Rekik, M.A. Mahjoub. Deep Random Forest-based Learning Transfer to SVM for Brain Tumor Segmentation. ATSIP (2016).
  25. 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).
  26. 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).
  27. 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).
  28. 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).


  29. 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).
  30. 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%).
  31. 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%).
  32. 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).


  33. 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).


  34. 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).
  35. 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).
  36. 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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