Informative Feature-Guided Siamese Network for Early Diagnosis of Autism.
Informative Feature-Guided Siamese Network for Early Diagnosis of Autism.
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DOI：
10.1007/978-3-030-59861-7_68
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发表时间：
2020-10
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自闭症或自闭症谱系障碍（ASD）是一种复杂的发育障碍，通常在3-4岁左右根据行为进行观察来诊断。研究表明，早期治疗，特别是在生命的前两年大脑发育早期，可以显著改善症状，因此，尽早识别ASD是很重要的。以往的研究大多采用基于成像的生物标志物进行ASD的早期诊断。然而，他们只关注从强度图像中提取特征，而忽略了从分割和分割地图中获得更多信息的指导。此外，由于自闭症受试者的数量总是比正常受试者少得多，这种班级不平衡问题使ASD的诊断更具挑战性。在这项工作中，我们提出了一个端到端的信息特征引导的Siamese网络，用于ASD的早期诊断。具体而言，除了T1w和T2w图像外，还使用分割图和分割图的判别特征来训练模型。为了缓解类不平衡问题，我们利用Siamese网络来有效地学习是什么让这对输入属于同一个类或不同的类。此外，该系统还结合了特定主题注意力模块，以端到端全自动学习方式识别自闭症相关区域。消融研究和对比均证明了该方法的有效性，总体准确率为85.4%，敏感性为80.8%，特异性为86.7%。
Autism, or autism spectrum disorder (ASD), is a complex developmental disability, and usually diagnosed with observations at around 3–4 years old based on behaviors. Studies have indicated that the early treatment, especially during early brain development in the first two years of life, can significantly improve the symptoms, therefore, it is important to identify ASD as early as possible. Most previous works employed imaging-based biomarkers for the early diagnosis of ASD. However, they only focused on extracting features from the intensity images, ignoring the more informative guidance from segmentation and parcellation maps. Moreover, since the number of autistic subjects is always much smaller than that of normal subjects, this class-imbalance issue makes the ASD diagnosis more challenging. In this work, we propose an end-to-end informative feature-guided Siamese network for the early ASD diagnosis. Specifically, besides T1w and T2w images, the discriminative features from segmentation and parcellation maps are also employed to train the model. To alleviate the class-imbalance issue, the Siamese network is utilized to effectively learn what makes the pair of inputs belong to the same class or different classes. Furthermore, the subject-specific attention module is incorporated to identify the ASD-related regions in an end-to-end fully automatic learning manner. Both ablation study and comparisons demonstrate the effectiveness of the proposed method, achieving an overall accuracy of 85.4%, sensitivity of 80.8%, and specificity of 86.7%.