Kwak Min Gu, Su Yi, Chen Kewei, Weidman David, Wu Teresa, Lure Fleming, Li Jing
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Banner Alzheimer's Institute, Phoenix, AZ 85006, USA.
Bioengineering (Basel). 2023 Sep 28;10(10):1141. doi: 10.3390/bioengineering10101141.
Early diagnosis of Alzheimer's disease (AD) is an important task that facilitates the development of treatment and prevention strategies, and may potentially improve patient outcomes. Neuroimaging has shown great promise, including the amyloid-PET, which measures the accumulation of amyloid plaques in the brain-a hallmark of AD. It is desirable to train end-to-end deep learning models to predict the progression of AD for individuals at early stages based on 3D amyloid-PET. However, commonly used models are trained in a fully supervised learning manner, and they are inevitably biased toward the given label information. To this end, we propose a selfsupervised contrastive learning method to accurately predict the conversion to AD for individuals with mild cognitive impairment (MCI) with 3D amyloid-PET. The proposed method, SMoCo, uses both labeled and unlabeled data to capture general semantic representations underlying the images. As the downstream task is given as classification of converters vs. non-converters, unlike the general self-supervised learning problem that aims to generate task-agnostic representations, SMoCo additionally utilizes the label information in the pre-training. To demonstrate the performance of our method, we conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The results confirmed that the proposed method is capable of providing appropriate data representations, resulting in accurate classification. SMoCo showed the best classification performance over the existing methods, with AUROC = 85.17%, accuracy = 81.09%, sensitivity = 77.39%, and specificity = 82.17%. While SSL has demonstrated great success in other application domains of computer vision, this study provided the initial investigation of using a proposed self-supervised contrastive learning model, SMoCo, to effectively predict MCI conversion to AD based on 3D amyloid-PET.
阿尔茨海默病（AD）的早期诊断是一项重要任务，有助于制定治疗和预防策略，并可能改善患者预后。神经影像学已显示出巨大潜力，包括淀粉样蛋白PET，它可测量大脑中淀粉样斑块的积累——这是AD的一个标志。期望训练端到端深度学习模型，以便根据三维淀粉样蛋白PET预测个体在早期阶段AD的进展。然而，常用模型是以完全监督学习的方式进行训练的，它们不可避免地会偏向于给定的标签信息。为此，我们提出一种自监督对比学习方法，以根据三维淀粉样蛋白PET准确预测轻度认知障碍（MCI）个体向AD的转化。所提出的方法SMoCo使用有标签和无标签数据来捕捉图像背后的一般语义表示。由于下游任务是转化者与非转化者的分类，与旨在生成与任务无关表示的一般自监督学习问题不同，SMoCo在预训练中还额外利用了标签信息。为了证明我们方法的性能，我们在阿尔茨海默病神经影像学倡议（ADNI）数据集上进行了实验。结果证实，所提出的方法能够提供适当的数据表示，从而实现准确分类。SMoCo在现有方法中表现出最佳的分类性能，AUROC = 85.17%，准确率 = 81.09%，灵敏度 = 77.39%，特异性 = 82.17%。虽然自监督学习在计算机视觉的其他应用领域已取得巨大成功，但本研究首次对使用所提出的自监督对比学习模型SMoCo基于三维淀粉样蛋白PET有效预测MCI向AD的转化进行了调查。
