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

TL;DR

This paper introduces a novel deep learning framework combining Path Signature, Siamese verification, and unsupervised feature compression to improve early autism diagnosis from structural MR images, addressing data scarcity and heterogeneity.

Contribution

It presents a new method integrating Path Signature and Siamese networks with unsupervised compression for early ASD detection, outperforming existing approaches.

Findings

Method outperforms existing machine learning techniques.

Provides anatomical insights for autism diagnosis.

Effective with scarce, imbalanced, and heterogeneous data.

Abstract

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.