National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China.
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore.
Med Image Anal. 2020 Jul;63:101662. doi: 10.1016/j.media.2020.101662. Epub 2020 Feb 1.
As a kind of neurodevelopmental disease, autism spectrum disorder (ASD) can cause severe social, communication, interaction, and behavioral challenges. To date, many imaging-based machine learning techniques have been proposed to address ASD diagnosis issues. However, most of these techniques are restricted to a single template or dataset from one imaging center. In this paper, we propose a novel multi-template multi-center ensemble classification scheme for automatic ASD diagnosis. Specifically, based on different pre-defined templates, we construct multiple functional connectivity (FC) brain networks for each subject based on our proposed Pearson's correlation-based sparse low-rank representation. After extracting features from these FC networks, informative features to learn optimal similarity matrix are then selected by our self-weighted adaptive structure learning (SASL) model. For each template, the SASL method automatically assigns an optimal weight learned from the structural information without additional weights and parameters. Finally, an ensemble strategy based on the multi- template multi-center representations is applied to derive the final diagnosis results. Extensive experiments are conducted on the publicly available Autism Brain Imaging Data Exchange (ABIDE) database to demonstrate the efficacy of our proposed method. Experimental results verify that our proposed method boosts ASD diagnosis performance and outperforms state-of-the-art methods.
作为一种神经发育性疾病，自闭症谱系障碍（ASD）会导致严重的社交、沟通、互动和行为挑战。迄今为止，已经提出了许多基于成像的机器学习技术来解决 ASD 诊断问题。然而，这些技术大多仅限于来自单一成像中心的单一模板或数据集。在本文中，我们提出了一种新颖的多模板多中心集成分类方案，用于自动 ASD 诊断。具体来说，我们基于不同的预定义模板，基于我们提出的基于 Pearson 相关的稀疏低秩表示方法，为每个受试者构建多个功能连接（FC）脑网络。从这些 FC 网络中提取特征后，我们的自加权自适应结构学习（SASL）模型选择学习最优相似矩阵的信息特征。对于每个模板，SASL 方法会自动分配从结构信息中学习到的最优权重，而无需额外的权重和参数。最后，基于多模板多中心表示的集成策略用于得出最终的诊断结果。我们在公开的自闭症脑成像数据交换（ABIDE）数据库上进行了广泛的实验，以证明我们提出的方法的有效性。实验结果验证了我们提出的方法提高了 ASD 诊断性能，并优于最先进的方法。
