Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor

Publication date: Jul 12, 2023

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.


Concepts Keywords
Autism Al
Bioengineering Asd
Freesurfer Autism
Gang_li Based
Timepoint Brain


Type Source Name
disease MESH Autism
disease MESH Autism Spectrum Disorder
disease MESH abnormalities
drug DRUGBANK Isoxaflutole
drug DRUGBANK Aspartame
drug DRUGBANK Trestolone
drug DRUGBANK Esomeprazole

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