Wang Yan, Gu Xuming, Hou Wenju, Zhao Meng, Sun Li, Guo Chunjie
Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun 130021, China.
Brain Sci. 2023 Feb 10;13(2):306. doi: 10.3390/brainsci13020306.
Deep learning has shown impressive diagnostic abilities in Alzheimer's disease (AD) research in recent years. However, although neuropsychological tests play a crucial role in screening AD and mild cognitive impairment (MCI), there is still a lack of deep learning algorithms only using such basic diagnostic methods. This paper proposes a novel semi-supervised method using neuropsychological test scores and scarce labeled data, which introduces difference regularization and consistency regularization with pseudo-labeling. A total of 188 AD, 402 MCI, and 229 normal controls (NC) were enrolled in the study from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We first chose the 15 features most associated with the diagnostic outcome by feature selection among the seven neuropsychological tests. Next, we proposed a dual semi-supervised learning (DSSL) framework that uses two encoders to learn two different feature vectors. The diagnosed 60 and 120 subjects were randomly selected as training labels for the model. The experimental results show that DSSL achieves the best accuracy and stability in classifying AD, MCI, and NC (85.47% accuracy for 60 labels and 88.40% accuracy for 120 labels) compared to other semi-supervised methods. DSSL is an excellent semi-supervised method to provide clinical insight for physicians to diagnose AD and MCI.
近年来，深度学习在阿尔茨海默病（AD）研究中展现出了令人印象深刻的诊断能力。然而，尽管神经心理学测试在AD和轻度认知障碍（MCI）的筛查中起着关键作用，但仍然缺乏仅使用此类基本诊断方法的深度学习算法。本文提出了一种使用神经心理学测试分数和稀缺标注数据的新型半监督方法，该方法引入了带有伪标签的差异正则化和一致性正则化。本研究从阿尔茨海默病神经影像倡议（ADNI）数据库中招募了188名AD患者、402名MCI患者和229名正常对照（NC）。我们首先通过在七项神经心理学测试中进行特征选择，选出了与诊断结果最相关的15个特征。接下来，我们提出了一个双半监督学习（DSSL）框架，该框架使用两个编码器来学习两个不同的特征向量。随机选择60名和120名已确诊的受试者作为模型的训练标签。实验结果表明，与其他半监督方法相比，DSSL在对AD、MCI和NC进行分类时实现了最佳的准确性和稳定性（60个标签时准确率为85.47%，120个标签时准确率为88.40%）。DSSL是一种出色的半监督方法，可为医生诊断AD和MCI提供临床见解。
