Alzheimer's Disease (AD), which is the most common cause of dementia, is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of the disease is crucial for making treatment decisions. However, most of the literature on computer-assisted detection of AD focuses on classifying brain images into one of three major categories: healthy, MCI, and AD; or categorising MCI patients into one of (1) progressive: those who progress from MCI to AD at a future examination time during a given study period, and (2) stable: those who stay as MCI and never progress to AD. This misses the opportunity to accurately identify the trajectory of progressive MCI patients. In this paper, we revisit the brain image classification task for AD identification and re-frame it as an ordinal classification task to predict how close a patient is to the severe AD stage. To this end, we select progressive MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and construct an ordinal dataset with a prediction target that indicates the time to progression to AD. We train a siamese network model to predict the time to onset of AD based on MRI brain images. We also propose a weighted variety of siamese networks and compare its performance to a baseline model. Our evaluations show that incorporating a weighting factor to siamese networks brings considerable performance gain at predicting how close input brain MRI images are to progressing to AD.

翻译：阿尔茨海默病（Alzheimer's Disease, AD）是痴呆症最常见的病因，其病程进展前会出现轻度认知障碍（Mild Cognitive Impairment, MCI）。疾病的早期检测对于制定治疗方案至关重要。然而，现有关于计算机辅助AD检测的大部分文献，主要集中于将脑部图像分为三大类：健康、MCI和AD；或对MCI患者进行二分类：（1）进展型——在研究期间未来某个检查时间点从MCI进展为AD的患者；（2）稳定型——始终处于MCI状态且未进展为AD的患者。这忽略了精确识别进展型MCI患者疾病轨迹的可能性。本文重新审视AD识别中的脑部图像分类任务，将其重构为序数分类任务，以预测患者距离重度AD阶段的接近程度。为此，我们选取阿尔茨海默病神经影像学倡议（Alzheimer's Disease Neuroimaging Initiative, ADNI）数据集中进展型MCI患者，构建以进展至AD的时间为预测目标的序数数据集。基于MRI脑部图像，我们训练孪生网络（siamese network）模型预测AD发病时间，并提出加权孪生网络变体，将其性能与基线模型进行比较。评估结果表明，在孪生网络中引入权重因子可显著提升预测输入脑部MRI图像距离AD进展时间接近程度的性能。