Publication Date

5-1-2016

Abstract

Despite the rapidly growing interest, progress in the study of relations between physiological abnormalities and mental disorders is hampered by complexity of the human brain and high costs of data collection. The complexity can be captured by machine learning approaches, but they still may require significant amounts of data. In this thesis, we seek to mitigate the latter challenge by developing a data driven sample generator model for the generation of synthetic realistic training data. Our method greatly improves generalization in classification of schizophrenia patients and healthy controls from their structural magnetic resonance images. A feed forward neural network trained exclusively on continuously generated synthetic data produces the best area under the curve compared to classifiers trained on real data alone.

Degree Name

Statistics

Level of Degree

Masters

Department Name

Mathematics & Statistics

First Committee Member (Chair)

Erik Barry Erhardt

Second Committee Member

Li Li

Third Committee Member

Marios Pattichis

Project Sponsors

The Mind Research Network, NVIDIA, National Institute of Health

Language

English

Keywords

machine learning, rejection sampling, multilayer perceptron, smri, schizophrenia

Document Type

Thesis

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