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. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

翻译：自闭症谱系障碍（ASD）正日益成为威胁公众健康的重大问题。早期诊断对于及时有效的干预和治疗至关重要。然而，基于交流和行为模式的传统诊断方法对2岁以下儿童并不可靠。鉴于自闭症婴幼儿存在神经发育异常的迹象，我们采用一种新颖的深度学习方法，从本身稀缺、类别不平衡且异质的结构磁共振图像中提取关键特征，用于早期自闭症诊断。具体而言，我们提出了一个孪生验证框架以扩展稀缺数据，以及一个无监督压缩器通过提取关键特征来缓解数据不平衡问题。我们还提出了权重约束，通过在验证过程中为不同样本赋予不同投票权重来应对样本异质性，并采用路径特征从纵向双时间点数据中揭示有意义的发育特征。我们进一步提取了机器学习聚焦的脑区用于自闭症诊断。大量实验表明，该方法在实际场景下表现优异，超越了现有机器学习方法，并为自闭症早期诊断提供了解剖学见解。