Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification

2023-04-18

IEEE International Symposium on Biomedical Imaging

AI Summary

To address over-parameterization and uncertainty in deep networks caused by limited samples in early-stage Alzheimer’s disease (AD) neuroimaging classification, this paper proposes a covariance-driven multi-scale scale-space representation framework. Methodologically, it innovatively integrates covariance modeling and scale-space theory into the neural network backbone, enabling compact representation of high-dimensional brain images and dual-space disentanglement of features and tasks. The framework incorporates scale-space convolution, covariance-based feature extraction, and multi-scale fusion, while supporting gradient-weighted, individualized localization of AD-affected brain regions. Evaluated on the ADNI dataset, the model achieves significantly improved classification accuracy, accelerated convergence, and a substantial reduction in parameter count. Crucially, it retains strong interpretability—precisely identifying AD-specific atrophic brain regions such as the hippocampus and entorhinal cortex.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Representation Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Addresses underdetermined systems in DNNs with limited training samples.
• Proposes a framework for efficient high-dimensional space representation.
• Improves Alzheimer's classification using multi-scale neuroimaging measures.

Innovation

• Utilizes scale-space theory with covariance structure
• Combines transform and classifier for multi-scale representation
• Enhances interpretability using gradient information