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Hooman Rokham
The Mind Research Network
Machine learningSupport vector machineTask analysisMood disordersComputer visionComputer scienceNeuroimagingBinary classificationFractional anisotropy
3Publications
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#2Haleh FalakshahiH-Index: 1
Last. Vince D. CalhounH-Index: 2
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Psychotic disorders such as schizophrenia and bipolar disorder are difficult to classify because they share overlapping symptoms. Deriving biomarkers of illness using structural MRI dataset are essential because they may lead to improved diagnosis. Previous studies typically predict the diagnosis labels using supervised classifiers that rely on truly labeled dataset. Mislabeled subjects may increase the complexity of the predictive model and may impact its performance. In this work, we address t...
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Objective: Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ). Methods: We start with estimat...
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Jul 1, 2019 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Anees Abrol (GSU: Georgia State University)
#2Hooman Rokham (The Mind Research Network)
Last. Vince D. Calhoun (GSU: Georgia State University)H-Index: 2
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In this work, we study the potential of the deep residual neural network (ResNet) architecture to learn abstract neuroanatomical alterations in the structural MRI data by evaluating its diagnostic and prognostic classification performance on two large, independent multi-group (ADNI and BSNIP) neuroimaging datasets. We conduct several binary classification tasks to assess the diagnostic/prognostic performance of the ResNet architecture through a rigorous, repeated and stratified k-fold cross-vali...
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