Wang Jun, Zhang Fengyexin, Jia Xiuyi, Wang Xin, Zhang Han, Ying Shihui, Wang Qian, Shi Jun, Shen Dinggang
School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Med Image Anal. 2022 Jan;75:102294. doi: 10.1016/j.media.2021.102294. Epub 2021 Oct 31.
The behavioral and cognitive deficits in autism spectrum disorder (ASD) patients are associated with abnormal brain function. The resting-state functional magnetic resonance imaging (rs-fMRI) is an effective non-invasive tool for revealing the brain dysfunction for ASD patients. However, most rs-fMRI based ASD diagnosis methods are developed for simple binary classification, instead of classification of multiple sub-types in ASD. Besides, they assume that the class boundary in ASD classification is crisp, whereas the symptoms of ASD sub-types are a continuum from mild to severe impairments in both social communication and restrictive repetitive behaviors/interests, and do not have crisp boundary between each other. To this end, we introduce label distribution learning (LDL) into multi-class ASD classification and propose LDL-CSCS under the LDL framework. Specifically, the label distribution is introduced to describe how individual disease labels correlate with the subject. In the learning crierion of LDL-CSCS, the label distribution is decomposed into the class-shared and class-specific components, in which the class-shared component records the common knowledge across all persons and the class-specific component records the specific information in each ASD sub-type. Low-rank constraint is imposed on the class-shared component whereas the group sparse constraint is imposed on the class-specific component, respectively. An Augmented Lagrange Method (ALM) is developed to find the optimal solution. The experimental results show that the proposed method for ASD diagnosis has superior classification performance, compared with some existing algorithms.
自闭症谱系障碍（ASD）患者的行为和认知缺陷与大脑功能异常有关。静息态功能磁共振成像（rs-fMRI）是一种揭示ASD患者大脑功能障碍的有效非侵入性工具。然而，大多数基于rs-fMRI的ASD诊断方法是为简单的二元分类而开发的，而不是用于ASD多种亚型的分类。此外，它们假设ASD分类中的类边界是清晰的，而ASD亚型的症状在社交沟通和限制性重复行为/兴趣方面是从轻度到重度损伤的连续体，彼此之间没有清晰的边界。为此，我们将标签分布学习（LDL）引入多类ASD分类，并在LDL框架下提出LDL-CSCS。具体来说，引入标签分布来描述个体疾病标签与受试者的关联方式。在LDL-CSCS的学习准则中，标签分布被分解为类共享和类特定组件，其中类共享组件记录所有人的共同知识，类特定组件记录每个ASD亚型中的特定信息。分别对类共享组件施加低秩约束，对类特定组件施加组稀疏约束。开发了一种增广拉格朗日方法（ALM）来找到最优解。实验结果表明，与一些现有算法相比，所提出的ASD诊断方法具有优越的分类性能。
