RClaNet: An Explainable Alzheimer's Disease Diagnosis Framework by Joint Registration and Classification

Alzheimer's disease (AD) is an irreversible neurodegenerative disease that affects people's ability of daily life. Unfortunately, there is currently no known cure for AD. Thus, the early detection of AD plays a key role in preventing and controlling its progression. As one of representative methods for measuring brain atrophy, image registration technique has been widely adopted for AD diagnosis. In this study, an AD assistant diagnosis framework based on joint registration and classification is proposed. Specifically, to capture more local deformation information, a novel patch-based joint brain image registration and classification network (RClaNet) to estimate the local dense deformation fields (DDF) and disease risk probability maps (DRM) that explain high-risk areas for AD patients. RClaNet consists of a registration network and a classification network, in which the deformation field from registration network is fed into the classification network to enhance the prediction accuracy of the disease. Then, the exponential distance weighting method is used to obtain the global DDF and the global DRM without grid-like artifacts. Finally, the global classification network uses the global DRM for the early detection of AD. We evaluate the proposed method on the OASIS-3, AIBL, ADNI and COVID-19 datasets, and experimental results show that the proposed RClaNet achieves superior registration performances than several state-of-the-art methods. Early diagnosis of AD using the global DRM also yielded competitive results. These experiments prove that the deformation information in the registration process can be used to characterize subtle changes of degenerative diseases and further assist clinicians in diagnosis.

I. Introduction

Alzheimer's disease (AD) is an insidious neurodegenerative disease that causes brain atrophy and cell death, and is the most common type of dementia [1]. However, there is currently no effective treatment that delays the onset or slows the progression of AD. Mild cognitive impairment (MCI) is conceptualized as a transitional state between normal aging and dementia. In the MCI stage, clinical practice has shown that enhancing patient care, quality of life, drug and mental treatment can markedly delay disease progression. Thus, accurate and timely diagnosis of MCI is critical to the early detection and treatment of AD [2].