A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases

TL;DR

This paper introduces a self-guided multimodal GNN that autonomously incorporates domain knowledge from natural language sources to improve Alzheimer's Disease diagnosis, enhancing both accuracy and interpretability.

Contribution

The paper presents a novel self-guided, knowledge-infused multimodal GNN that automatically integrates domain knowledge without manual expert intervention for AD analysis.

Findings

Improves GNN performance on AD datasets

Provides interpretable graph-based explanations

Demonstrates effective knowledge extraction from literature

Abstract

Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzing brain connectomes in Alzheimer's Disease (AD), highlighting the need to incorporate domain knowledge for optimal performance. Infusing AD-related knowledge into GNNs is a complicated task. Existing methods typically rely on collaboration between computer scientists and domain experts, which can be both time-intensive and resource-demanding. To address these limitations, this paper presents a novel self-guided, knowledge-infused multimodal GNN that autonomously incorporates domain knowledge into the model development process. Our approach conceptualizes domain knowledge as natural language and introduces a specialized multimodal GNN capable of leveraging this uncurated knowledge to guide the learning process of the GNN, such that it can improve the model performance and strengthen the interpretability of the predictions. To evaluate our framework, we curated a comprehensive dataset of recent peer-reviewed papers on AD and integrated it with multiple real-world AD datasets. Experimental results demonstrate the ability of our method to extract relevant domain knowledge, provide graph-based explanations for AD diagnosis, and improve the overall performance of the GNN. This approach provides a more scalable and efficient alternative to inject domain knowledge for AD compared with the manual design from the domain expert, advancing both prediction accuracy and interpretability in AD diagnosis.