Learning interpretable continuous representation for Alzheimer’s Disease classification

Alzheimer’s disease (AD) is the leading cause of dementia worldwide, characterized by its gradual progression and the subtle variations across disease stages, which pose significant challenges for accurate diagnosis. While deep representation learning algorithms have shown promise in the early detection of AD using MRI data, existing approaches often overlook the meaningful relationships between continuous labels in AD progression, and the learned representations frequently lack interpretability due to the black-box nature of deep learning models. To address these limitations, we propose ICReL (Interpretable Continuous Representation Learning), a novel concept based on coding rate principles that captures continuous representations across AD stages while maintaining a high degree of interpretability. Extensive experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that ICReL not only outperforms multiple baseline methods using 2D slice or 3D MRI in terms of learning continuous representations, but also exhibits enhanced robustness to label corruption and superior predictive performance. This work offers a new, interpretable approach to representation learning for computer-aided diagnosis of neurodegenerative diseases.