Alzheimer's Disease Clinical Scores Prediction based on the Label Distribution Learning using Brain Structural MRI

Hui Chen, Hezhe Qiao, Fan Zhu, Lin Chen

Abstract: Predicting Alzheimer's disease (AD) clinical scores offers a useful tool to monitor dementia progression at different time points. Previous machine learning methods usually focused on regressing the clinical scores directly but ignored the ambigu-ous information among score labels. In this study, we introduce a novel AD clinical scores prediction framework based on label distribution learning (LDL), named CSP-LDL. Notably, we first turn the clinical scores into a normal probability distribution of discrete labels to exploit uncertainty among the dementia scores. Then we learn the distribution of discrete labels by optimizing Kullback-Leibler (KL) divergence between the estimated and ground-truth distributions using a 3D CNN. Moreover, we further employ an expectation regression layer to regress clinical score value at the fine-grained level based on the predicted label distribution. Experiments on ADNI-1 and ADNI-2 datasets show that our CSP-LDL model outperforms existing state-of-the-art methods in terms of dementia regression accuracy at multiple time points using baseline structural magnetic resonance imaging (sMRI) data, demonstrating its effectiveness in early clinical diagnosis of AD.