Alzheimer's Disease (AD) is a progressive disease preceded by Mild Cognitive Impairment (MCI). Early detection of AD 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 categorizing MCI patients into (1) progressive: those who progress from MCI to AD at a future examination time, 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 network 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. Moreover, we complement our results with an interpretation of the learned embedding space of the Siamese networks using a model explainability technique.

翻译：阿尔茨海默病（AD）是一种进行性疾病，其前驱阶段为轻度认知障碍（MCI）。AD的早期检测对于制定治疗决策至关重要。然而，现有计算机辅助AD检测文献大多将脑部图像分为三大类：健康、MCI和AD；或将MCI患者分为两类：（1）进展型：未来检查时由MCI进展为AD的患者；（2）稳定型：始终保持MCI且从未进展为AD的患者。这种做法错失了精确识别进展型MCI患者疾病轨迹的机会。本文重新审视AD识别的脑部图像分类任务，将其重构为有序分类任务，以预测患者距离严重AD阶段的接近程度。为此，我们从阿尔茨海默病神经影像学倡议（ADNI）数据集中选取进展型MCI患者，构建具有预测目标（即进展至AD的时间）的有序数据集。我们训练孪生网络模型，基于MRI脑部图像预测AD发病时间。同时提出一种加权孪生网络变体，并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对输入脑部MRI图像接近AD进展程度的预测能力。此外，我们运用模型可解释性技术解析孪生网络学习到的嵌入空间，为结果提供补充说明。