Date of Award
Spring 5-2026
Document Type
Thesis
Department
Electrical Engineering and Computer Science
First Advisor
Yuxin Wen, Ph.D.
Second Advisor
Thomas C. Piechota, Ph.D.
Third Advisor
Chelsea Parlett-Pelleriti, Ph.D.
Fourth Advisor
Trudi Qi, Ph.D.
Abstract
Accurate diagnosis and progression modeling of Alzheimer’s Disease (AD) are critical for effective intervention, disease monitoring, and patient care. Traditional approaches rely on a single modality, such as clinical assessments, neuroimaging, or genetic markers, but may fail in capturing the complex, multifaceted nature of AD. Therefore, multimodal learning addresses this limitation by integrating complementary information across sources, but conventional fusion strategies, such as early feature concatenation and late decision-level fusion, often model modalities independently and fail to capture high-order cross-modal interactions that are essential for reliable diagnosis and progression modeling. To address these limitations, we propose two complementary multimodal frameworks that together advance AD diagnosis and longitudinal risk assessment. For diagnosis, we propose a Multimodal Tensor Fusion Network (MTFN) that integrates heterogeneous data sources, including visual imagery, demographics, and longitudinal time-series data. This approach leverages tensor representations to model cross-modal interactions while preserving structural dependencies within each modality. For progression modeling, we propose a Multimodal Supervised Temporal Encoder Joint Modeling network (MSTE-JM) that uses a supervised temporal encoder to learn compact multimodal representations, which are subsequently incorporated into a Bayesian joint model linking longitudinal cognitive measurements with time-to-event outcomes.
Experiments on publicly available AD datasets demonstrate that MTFN outperforms deep learning classifiers in diagnostic accuracy, while MSTE-JM produces robust progression estimates that capture the temporal dynamics of cognitive decline and the risk of conversion. Together, these proposed frameworks establish tensor-based multimodal learning as a powerful contribution for advancing both accurate detection and long-term risk stratification of neurodegenerative diseases.
DOI
10.36837/chapman.000733
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
T. T. Le, "Multimodal learning for disease diagnosis and progression modeling," M. S. thesis, Chapman University, Orange, CA, 2026. https://doi.org/10.36837/chapman.000733
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Longitudinal Data Analysis and Time Series Commons, Neurosciences Commons, Survival Analysis Commons