Autism Spectrum Disorder (ASD) has been emerging as a growing public health
threat. Early diagnosis of ASD is crucial for timely, effective intervention
and treatment. However, conventional diagnosis methods based on communications
and behavioral patterns are unreliable for children younger than 2 years of
age. Given evidences of neurodevelopmental abnormalities in ASD infants, we
resort to a novel deep learning-based method to extract key features from the
inherently scarce, class-imbalanced, and heterogeneous structural MR images for
early autism diagnosis. Specifically, we propose a Siamese verification
framework to extend the scarce data, and an unsupervised compressor to
alleviate data imbalance by extracting key features. We also proposed weight
constraints to cope with sample heterogeneity by giving different samples
different voting weights during validation, and we used Path Signature to
unravel meaningful developmental features from the two-time point data
longitudinally. Extensive experiments have shown that our method performed well
under practical scenarios, transcending existing machine learning methods.

针对 2 岁以下儿童的传统的基于交流和行为模式的诊断方法不够可靠，因此我们提出了一种深度学习的方法来提取关键特征从病态的、类不平衡和异质的结构 MR 图像中进行早期的自闭症诊断，该方法通过 Siamese 验证框架扩展稀缺数据，通过无监督压缩器抑制数据不平衡，通过权重约束解决样本异质性，并使用 Path Signature 来纵向解读两个时间点的数据，以此有效的完成了早期自闭症的诊断。