Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor
This work addresses the problem of unreliable early autism diagnosis for children under 2 years old, offering a novel deep learning approach with anatomical insights, though it appears incremental in its methodological contributions.
The authors tackled early autism diagnosis from scarce, imbalanced structural MRI data by proposing a Siamese verification framework, unsupervised compressor, weight constraints, and Path Signature feature extraction, achieving performance that transcended existing machine learning methods.
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.