Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification
Journal:
arXiv
Published Date:
Mar 3, 2025
Abstract
Stacking excessive layers in DNN results in highly underdetermined system
when training samples are limited, which is very common in medical
applications. In this regard, we present a framework capable of deriving an
efficient high-dimensional space with reasonable increase in model size. This
is done by utilizing a transform (i.e., convolution) that leverages scale-space
theory with covariance structure. The overall model trains on this transform
together with a downstream classifier (i.e., Fully Connected layer) to capture
the optimal multi-scale representation of the original data which corresponds
to task-specific components in a dual space. Experiments on neuroimaging
measures from Alzheimer's Disease Neuroimaging Initiative (ADNI) study show
that our model performs better and converges faster than conventional models
even when the model size is significantly reduced. The trained model is made
interpretable using gradient information over the multi-scale transform to
delineate personalized AD-specific regions in the brain.
