Zhang Xiangfei, Shams Shayel Parvez, Yu Hang, Wang Zhengxia, Zhang Qingchen
School of Cyberspace Security, Hainan University, Haikou 570228, China.
School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264005, China.
Diagnostics (Basel). 2023 Jan 6;13(2):218. doi: 10.3390/diagnostics13020218.
Autism spectrum disorder (ASD) is a lifelong neurological disease, which seriously reduces the patients' life quality. Generally, an early diagnosis is beneficial to improve ASD children's life quality. Current methods based on samples from multiple sites for ASD diagnosis perform poorly in generalization due to the heterogeneity of the data from multiple sites. To address this problem, this paper presents a similarity measure-based approach for ASD diagnosis. Specifically, the few-shot learning strategy is used to measure potential similarities in the RS-fMRI data distributions, and, furthermore, a similarity function for samples from multiple sites is trained to enhance the generalization. On the ABIDE database, the presented approach is compared to some representative methods, such as SVM and random forest, in terms of accuracy, precision, and F1 score. The experimental results show that the experimental indicators of the proposed method are better than those of the comparison methods to varying degrees. For example, the accuracy on the TRINITY site is more than 5% higher than that of the comparison method, which clearly proves that the presented approach achieves a better generalization performance than the compared methods.
自闭症谱系障碍（ASD）是一种终身神经疾病，严重降低患者的生活质量。一般来说，早期诊断有利于提高自闭症谱系障碍儿童的生活质量。目前基于多站点样本的自闭症谱系障碍诊断方法，由于多站点数据的异质性，泛化性能较差。为了解决这个问题，本文提出了一种基于相似性度量的自闭症谱系障碍诊断方法。具体而言，采用少样本学习策略来度量静息态功能磁共振成像（RS-fMRI）数据分布中的潜在相似性，此外，还训练了一个针对多站点样本的相似性函数以增强泛化能力。在ABIDE数据库上，将本文提出的方法与一些代表性方法（如支持向量机（SVM）和随机森林）在准确率、精确率和F1分数方面进行了比较。实验结果表明，所提方法的实验指标在不同程度上优于比较方法。例如，在TRINITY站点上的准确率比比较方法高出5%以上，这清楚地证明了本文提出的方法比比较方法具有更好的泛化性能。
