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

arXiv: https://arxiv.org/abs/2307.06472 · HTML: https://mlsanity.com/paper/2307.06472/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 62 references that are in the map.
- First posted on arXiv 12 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data](https://mlsanity.com/paper/1904.07577/) (2019)

## Built on by (most cited first, 1 of 1)

- [Deep Learning for Longitudinal Medical Imaging: A Scoping Review](https://mlsanity.com/paper/2610.08838/) (2026)

## Abstract (arXiv)

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.