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Modeling Task fMRI Data Via Deep Convolutional Autoencoder., , , , , , , and . IEEE Trans. Medical Imaging, 37 (7): 1551-1561 (2018)Recognizing Brain States Using Deep Sparse Recurrent Neural Network., , , , , , , and . IEEE Trans. Medical Imaging, 38 (4): 1058-1068 (2019)Genetic Influence on Gyral Peaks., , , , , , , , , and . NeuroImage, (October 2023)Modeling Brain Diverse and Complex Hemodynamic Response Patterns via Deep Recurrent Autoencoder., , , , , , and . IEEE Trans. Cogn. Dev. Syst., 12 (4): 733-743 (2020)Latent source mining in FMRI data via deep neural network., , , , , , and . ISBI, page 638-641. IEEE, (2016)Multple-demand system identification and characterization via sparse representations of fMRI data., , , , and . ISBI, page 70-73. IEEE, (2016)Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)., , , , , , , and . MICCAI (3), volume 11072 of Lecture Notes in Computer Science, page 181-189. Springer, (2018)Novel approach of combining temporal segmentation results to the region-binding process for separating moving objects from still background., , and . VCIP, volume 4067 of Proceedings of SPIE, page 1066-1075. SPIE, (2000)Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data., , , , and . MMMI@MICCAI, volume 11977 of Lecture Notes in Computer Science, page 26-34. Springer, (2019)Morphological Analysis of the Left Ventricular Endocardial Surface and Its Clinical Implications., , , , , and . MICCAI (2), volume 7511 of Lecture Notes in Computer Science, page 502-510. Springer, (2012)