ConASD: Contrastive Few Shot Learning for Detecting Autism Spectrum Disorder via Eye Tracking Scanpath

Abstract Detecting Autism Spectrum Disorder (ASD) using Eye Tracking (ET) datasets is a challenging task and has been a long-standing problem. Recently, there has been a trend of developing ASD diagnosis models based on machine learning (ML), especially deep learning techniques. In this paper, we show that these existing methods still struggle to make accurate diagnoses in few-shot learning (FSL) settings, where the data available for training is limited in amount and imbalanced in nature. To address this challenge, we propose a model, named ConASD, for effective diagnosis of ASD under the FSL setting. The proposed model is a two-stage framework: it first trains an encoder for ET images using supervised contrastive learning, followed by fine-tuning a classifier for final diagnosis. With the contrastive learning strategy, the pre-trained encoder can better capture the discriminative features of the eye-tracking images, even with limited training data, and ultimately leads to better diagnosis accuracy and better generalization to unseen data. We evaluate the proposed ConASD model using two real-world ET datasets. The results demonstrate that ConASD outperforms existing approaches, particularly in few-shot scenarios, by up-to 7% improvement in terms of F1 scores. The results in this paper highlight the potential of using contrastive learning as a powerful tool, particularly in real-world medical scenarios where class imbalance is frequent and the data is limited.