Activity-Based Early Autism Diagnosis Using a Multi-Dataset Supervised Contrastive Learning Approach
Autism Spectrum Disorder (ASD) is a neurological disorder. Its primary symptoms include difficulty in verbal/non-verbal communication and rigid/repetitive behavior. Traditional methods of autism diagnosis require multiple visits to a human specialist. However, this process is generally time-consuming and may result in a delayed (early) intervention. In this paper, we present a data-driven approach to automate autism diagnosis using video clips of subjects performing simple activities recorded in a weakly constrained environment. This task is particularly challenging since the available training data is small, videos from the two categories ("ASD" and "Control") are generally perceptually indistinguishable, and there is no clear understanding of what features would be beneficial in this task. To address these, we present a novel multi-dataset supervised contrastive learning technique to learn discriminative features simultaneously from multiple video datasets with significantly diverse distributions. Extensive empirical analyses demonstrate the promise of our approach compared to competing techniques on this challenging task.