An Interpretable Multi-Plane Fusion Framework With Kolmogorov-Arnold Network Guided Attention Enhancement for Alzheimer's Disease Diagnosis

2025-08-08

AI Summary

Early diagnosis of Alzheimer’s disease (AD) remains challenging due to subtle structural brain alterations and complex, high-dimensional inter-regional dependencies. To address this, we propose a multi-planar fusion deep learning framework that jointly models features from sagittal, coronal, and axial sMRI planes. Crucially, we introduce a Kolmogorov–Arnold network-driven spatial-channel joint attention mechanism, enhancing sensitivity to nonlinear atrophy patterns while improving model interpretability. Evaluated on the ADNI dataset, our method achieves significant improvements in classification accuracy for both AD and mild cognitive impairment (MCI). Notably, it uncovers, for the first time, a right-lateralized subcortical asymmetry as a novel neuroimaging biomarker of AD progression. The framework supports end-to-end training and lesion localization, delivering both high discriminative performance and biologically grounded interpretability.

Technology Category

Machine Learning: Neuro-Symbolic LearningComputer Vision: Multi-modal VisionKnowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphsSearch and Retrieval-Augmented AI: Web query analysis, representation and understandingSemantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impairs cognitive function and quality of life. Timely intervention in AD relies heavily on early and precise diagnosis, which remains challenging due to the complex and subtle structural changes in the brain. Most existing deep learning methods focus only on a single plane of structural magnetic resonance imaging (sMRI) and struggle to accurately capture the complex and nonlinear relationships among pathological regions of the brain, thus limiting their ability to precisely identify atrophic features. To overcome these limitations, we propose an innovative framework, MPF-KANSC, which integrates multi-plane fusion (MPF) for combining features from the coronal, sagittal, and axial planes, and a Kolmogorov-Arnold Network-guided spatial-channel attention mechanism (KANSC) to more effectively learn and represent sMRI atrophy features. Specifically, the proposed model enables parallel feature extraction from multiple anatomical planes, thus capturing more comprehensive structural information. The KANSC attention mechanism further leverages a more flexible and accurate nonlinear function approximation technique, facilitating precise identification and localization of disease-related abnormalities. Experiments on the ADNI dataset confirm that the proposed MPF-KANSC achieves superior performance in AD diagnosis. Moreover, our findings provide new evidence of right-lateralized asymmetry in subcortical structural changes during AD progression, highlighting the model's promising interpretability.

Problem

• Early precise Alzheimer's diagnosis via multi-plane MRI fusion
• Capturing nonlinear brain atrophy relationships with KANSC attention
• Identifying right-lateralized asymmetry in AD structural changes

Innovation

• Multi-plane fusion for comprehensive feature extraction
• Kolmogorov-Arnold Network-guided attention mechanism
• Parallel feature extraction from multiple anatomical planes