Charles Anthony Ellis

BME PhD Defense Presentation

Date: 2023-04-03
Time: 4-6pm ET
Location / Meeting Link: https://emory.zoom.us/j/93784431797

Committee Members:
Dr. Vince Calhoun (advisor); Dr. May Wang; Dr. Gari Clifford; Dr. Robyn Miller; Dr. Sergey Plis


Title: Novel Explainability Approaches for Analyzing Functional Neuroinformatics Data with Supervised and Unsupervised Machine Learning

Abstract:
The use of artificial intelligence in healthcare is growing increasingly common. However, the use of artificial intelligence within the context of neuropsychiatric settings with functional neuroinformatics modalities like electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is still in its infancy. While there are a number of obstacles preventing the development of diagnostic and prognostic tools for clinical neuroinformatics, a key obstacle is the lack of explainability approaches uniquely adapted to the field. This lack of explainability methods has implications both within a clinical context and within a biomedical research context. In this dissertation, we propose a series of novel explainability approaches for systematically evaluating what deep learning models trained for both classification and clustering have learned from raw EEG data. These explainability approaches provide insight into key spatial, spectral, temporal, and interaction features uncovered by models. Within the context of fMRI, this dissertation expands upon existing explainability approaches for neuroimaging classification by combining them with approaches that estimate the degree of model confidence in predictions. This dissertation further presents several novel explainability approaches for insight into both hard and soft clustering algorithms applied to fMRI data.