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A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders.

, , , , , , , and . NeuroImage, (2015)

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Characterization of groups using composite kernels and multi-source fMRI analysis data: Application to schizophrenia., , , , and . NeuroImage, 58 (2): 526-536 (2011)A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: Application to schizophrenia., , , , and . NeuroImage, (2014)Deep independence network analysis of structural brain imaging: A simulation study., , , , , and . MLSP, page 1-6. IEEE, (2015)Three-way (N-way) fusion of brain imaging data based on mCCA + jICA and its application to discriminating schizophrenia., , , , , , , , , and 3 other author(s). NeuroImage, (2013)Generation of synthetic structural magnetic resonance images for deep learning pre-training., , , , and . ISBI, page 1057-1060. IEEE, (2015)A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders., , , , , , , and . NeuroImage, (2015)Combination of FMRI-SMRI-EEG data improves discrimination of schizophrenia patients by ensemble feature selection., , , , , , , and . EMBC, page 3889-3892. IEEE, (2014)The tenth annual MLSP competition: Schizophrenia classification challenge., , , , , , , and . MLSP, page 1-6. IEEE, (2014)Elastic deformations for data augmentation in breast cancer mass detection., , and . BHI, page 230-234. IEEE, (2018)Identification of patterns of gray matter abnormalities in schizophrenia using source-based morphometry and bagging., , , , , and . EMBC, page 1513-1516. IEEE, (2014)