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Unsupervised quantification of abdominal fat from CT images using Greedy Snakes., , , , and . Medical Imaging: Image Processing, volume 10133 of SPIE Proceedings, page 101332T. SPIE, (2017)Classification of schizophrenia and bipolar patients using static and time-varying resting-state FMRI brain connectivity., , , , , , and . ISBI, page 251-254. IEEE, (2015)Deep 3D convolution neural network for CT brain hemorrhage classification., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105751C. SPIE, (2018)Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity., , , , , , and . NeuroImage, (2016)Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images., , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 10134 of SPIE Proceedings, page 101340K. SPIE, (2017)Automatic Classification of Radiological Report for Intracranial Hemorrhage., , , , and . ICSC, page 187-190. IEEE, (2019)Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls., , , and . NeuroImage, (2017)Sex and Age Effects of Functional Connectivity in Early Adulthood., , , , , and . Brain Connect., 6 (9): 700-713 (2016)Impact of autocorrelation on functional connectivity., , , , , , , , , and 1 other author(s). NeuroImage, (2014)Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration., , , , , , and . npj Digit. Medicine, (2018)