HomoNet: Enhancing Multi-Center Alzheimer's Disease Classification via Disentangling Homogeneous Features in Structural MRI

Motivation: Variations in imaging conditions across centers challenge accurate diagnosis of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) using structural MRI (sMRI). Goal(s): Develop a robust and scalable model that accurately classifies AD, MCI, and normal controls (NC) across multi-center sMRI datasets without re-training on new data. Approach: This study presents "HomoNet", a model employing weakly-supervised disentangling learning to separate homogeneous (center-invariant) and heterogeneous (center-specific) features. Results: HomoNet outperforms current methods, achieving 74.37% accuracy in distinguish NC, MCI, and AD on an external dataset, enhancing diagnostic robustness across diverse multi-center MRI data. Impact: HomoNet enhances multi-center sMRI image classification, improving diagnostic accuracy for AD and MCI. It effectively addresses data heterogeneity, increases model generalizability, and provides a scalable, re-training-free solution, making it highly applicable for real-world clinical imaging.