Advisory Committee Chair
Murat M Tanik
Advisory Committee Members
Leon Jololian
Kent Keyser
Buren E Wells
Document Type
Dissertation
Date of Award
2020
Degree Name by School
Doctor of Philosophy (PhD) School of Engineering
Abstract
The Parkinson's Progression Markers Initiative (PPMI) dataset (which includes several types of imaging datasets) was created to generate biomarkers for diagnosing the presence and severity of Parkinson’s Disease (PD). Unlike some conditions, PD is not intuitively diagnosed directly from brain imaging. As such, it is vital to understand as well as classify PD. Deep learning techniques are becoming increasingly prevalent in medical imaging workflows. In some cases, deep learning outperforms human diagnostic ability. However, the black-box nature of many machine learning models can be troubling, particularly in a medical context since the results could imply alternate treatments and a human caregiver must ultimately take responsibility for diagnosis and treatment. Black-box algorithms provide only results, with no information regarding the process leading to that result. The work presented in this dissertation advances machine learning techniques through developing an augmented framework for comparing machine learning predictions with statistical methods of classification currently used in medical imaging. This provides a basis for determining if deep learning architectures, pre-processing steps, or dataset augmentations have desired effects on predictions. Developing a framework for a) validating machine learning models against other techniques and b) describing what the machine learning model prediction is based off of can be beneficial for adding transparency to a traditionally opaque process. As a second contribution of the presented dissertation, a novel, efficient, model-agnostic, and visualization method of black box models was developed. This novel visualization method is a novel approach to occlusion sensitivity which adds the notion of hierarchy and reduces the iterations required by three orders of magnitude. For PD, using deep learning workflows which treat a model as a black box have no potential to develop biomarker information through training and classification alone. Using the framework presented in this dissertation, PPMI datasets were used to train a PD classification model. The framework and visualization method were then applied to the resultant machine learning model to augment the classification provided with population level information from visualizations and voxel-wise statistics to provide a basis for developing information towards biomarker discovery and model validation, beyond simply developing a classifier.
Recommended Citation
Monroe, William Stonewall, "An Augmented Framework For Validating Neural Network Predictions Based On Statistical Modeling" (2020). All ETDs from UAB. 865.
https://digitalcommons.library.uab.edu/etd-collection/865