318 Hanes Hall, CB #3260 Chapel Hill, NC 27599-3260
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Ph.D. Defense: Leo Liu

May 10, 2018 @ 10:00 am - 12:00 pm

Ph.D. Thesis Defense

Public Presentation

Thursday, May 10th, 2018

125 Hanes Hall
10:00 AM

Leo Yu-Feng Liu


Advanced Statistical Learning Techniques for High-Dimensional Imaging Data


With the rapid development of neuroimaging techniques, scientists are interested in identifying imaging biomarkers that are related to different subtypes or transitional stages of various cancers, neuropsychiatric diseases, and neurodegenerative diseases. The scalar-on-image models have been proven to demonstrate good performance in such tasks. However, due to their high dimensionality, traditional methods may not work well in the estimation of such models. Some existing penalization methods may improve the performance but fail to take the complex spatial structure of the neuroimaging data into account. In the past decade, the spatially regularized methods have been popular due to their good performance in terms of both estimation and prediction. Despite the progress, many challenges still remain. In particular, most of the existing image classification methods focus on binary classification and consequently may underperform for the tasks of classifying diseases with multiple subtypes or transitional stages. Moreover, neuroimaging data usually present significant heterogeneity across subjects. As a result, existing methods for homogeneous data may fail. In this presentation, I will introduce several new statistical learning techniques to tackle these issues, including the Spatial Multi-category Angle based Classifier (SMAC), the Subject Variant Scalar-on-Image Regression (SVSIR) model and the Masking Convolutional Neural Network (MCNN). Extensive simulation studies and practical applications in neuroscience are presented to demonstrate the effectiveness of these proposed methods.


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May 10, 2018
10:00 am - 12:00 pm
Event Category:


Hanes Hall
Hanes Hall
Chapel Hill, NC 27599 United States