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Multivariate hurst exponent estimation in FMRI. Application to brain decoding of perceptual learning.

, , , , , and . ISBI, page 996-1000. IEEE, (2016)

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Automatic estimation of pelvic organ anatomical references., , , and . EMBC, page 5124-5127. IEEE, (2011)Uterus segmentation in dynamic MRI using LBP texture descriptors., , , and . Medical Imaging: Image Processing, volume 9034 of SPIE Proceedings, page 90343W. SPIE, (2014)A shape descriptors comparison for organs deformation sequence characterization in MRI sequences., , , and . ICIP, page 1069-1072. IEEE, (2009)A Diffeomorphic Mapping Based Characterization of Temporal Sequences: Application to the Pelvic Organ Dynamics Assessment., , , and . J. Math. Imaging Vis., 47 (1-2): 151-164 (2013)Impact of perceptual learning on resting-state fMRI connectivity: A supervised classification study., , and . EUSIPCO, page 250-254. IEEE, (2016)Benchmarking functional connectome-based predictive models for resting-state fMRI., , , , , , and . NeuroImage, (2019)Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation., , and . Medical Image Anal., (2019)HAD-Net: Hybrid Attention-based Diffusion Network for Glucose Level Forecast., and . CoRR, (2021)Pelvic Organs Dynamic Feature Analysis for MRI Sequence Discrimination., , , and . ICPR, page 2496-2499. IEEE Computer Society, (2010)A Diffeomorphic Matching Based Characterization of the Pelvic Organ Dynamics., , , and . CAIP (1), volume 6854 of Lecture Notes in Computer Science, page 469-476. Springer, (2011)