Mashhadi, N. (CSE) – Compositional, Clinically Conditioned, and Confound-Aware Deep Learning for Alzheimer’s Disease Neuroimaging

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia. Neuroimaging and clinical biomarkers can reveal early disease changes, but building reliable machine learning models is difficult because data come from different scanners and sites, some modalities are missing, labeled cohorts are limited, and factors such as age and scanner/site effects can bias results.

This dissertation develops machine learning methods for robust, interpretable, and controllable analysis of AD-related neuroimaging data. First, I introduce a modular, graph-based framework for multimodal AD detection that treats datasets and models as nodes and directed edges that can be combined to build more complex predictors. Second, I propose a clinically conditioned 3D VAE-GAN to synthesize brain MRI, enhanced with diffusion-driven sampling in clinical feature space to improve realism and control, supporting data augmentation. Third, I present a disentangled 3D masked autoencoder (MAE) that learns separated representations for age, pathology, and scanner effects, making it possible to isolate and adjust age, pathology, or scanner effects, while remaining reliable across sites.

Together, these contributions advance practical methods for modular prediction, controllable image generation, and confound-aware representation learning in neuroimaging, with an emphasis on generalization and interpretability for clinically relevant applications.

Event Host: Najmeh Mashhadi, Ph.D. Candidate, Computer Science and Engineering

Advisor: Razvan Marinescu

Zoom- https://ucsc.zoom.us/j/98195204428?pwd=nyfvbmd9t81Xj5Z3yPPVtu4R58CXHq.1

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