  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.
