Multimodal Multi-label Transfer Learning for Early Diagnosis of Alzheimer’s Disease

Abstract

Recent machine learning based studies for early Alzheimer’s disease (AD) diagnosis focus on the joint learning of both regression and classification tasks. However, most of existing methods only use data from a single domain, and thus cannot utilize the intrinsic useful correlation information among data from correlated domains. Accordingly, in this paper, we consider the joint learning of multi-domain regression and classification tasks with multimodal features for AD diagnosis. Specifically, we propose a novel multimodal multi-label transfer learning framework, which consists of two key components: 1) a multi-domain multi-label feature selection (MDML) model that selects the most informative feature subset from multi-domain data, and 2) multimodal regression and classification methods that can predict clinical scores and identify the conversion of mild cognitive impairment (MCI) to AD patients, respectively. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database show that the proposed method help improve the performances of both clinical score prediction and disease status identification, compared with the state-of-the-art methods.