Hierarchical graph-guided contextual representation learning for Neurodegenerative pattern recognition in MRI.
Authors
Affiliations (3)
Affiliations (3)
- School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, India.
- School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, Tamil Nadu, India. Electronic address: [email protected].
- School of Information and Data Sciences, Nagasaki University, Nagasaki, Japan.
Abstract
Neurodegenerative (ND) diseases are autoimmune diseases that affect the central nervous system, including the brain and spinal cord. In recent years, deep learning has demonstrated its potential in medical imaging for diagnostic purposes. However, for these techniques to be fully accepted in clinical settings, they must achieve high performance and gain the confidence of medical professionals regarding their interpretability. Therefore, an interpretable model should make decisions based on clinically relevant information like a domain expert. To achieve this, we present an interpretable classifier dedicated to the most common autoimmune ND diseases. The lesions associated with ND diseases exhibit irregular distributions and spatial dependencies in different regions of the brain, challenging traditional models to effectively capture both local and global relationships. To address this issue, we present a Residual Graph Neural Network enhanced Vision Transformer (RG-ViT) that represents MRI data as a graph of interconnected patches. By integrating residual connections into the GNN framework, we preserve critical features while promoting effective message passing. This approach overcomes the problem of spatial disconnection prevalent in standard patch-based methods and provides a cohesive and context-aware analysis of MRI data. Experimental results in detecting multiple sclerosis (MS), Parkinson's (PD), and Alzheimer's disease (AD) demonstrated our approach's consistent accuracy scores of 98.7%, 99.6%, and 99.1%, respectively. On the combined dataset for the global classification of ND diseases, it achieved an F1 score of 99.2%, justifying its generalizability.