Autism, also known as Autism Spectrum Disorder (or ASD), is a neurological disorder. Its main symptoms include difficulty in (verbal and/or non-verbal) communication, and rigid/repetitive behavior. These symptoms are often indistinguishable from a normal (control) individual, due to which this disorder remains undiagnosed in early childhood leading to delayed treatment. Since the learning curve is steep during the initial age, an early diagnosis of autism could allow to take adequate interventions at the right time, which might positively affect the growth of an autistic child. Further, the traditional methods of autism diagnosis require multiple visits to a specialized psychiatrist, however this process can be time-consuming. In this paper, we present a learning based approach to automate autism diagnosis using simple and small action video clips of subjects. This task is particularly challenging because the amount of annotated data available is small, and the variations among samples from the two categories (ASD and control) are generally indistinguishable. This is also evident from poor performance of a binary classifier learned using the cross-entropy loss on top of a baseline encoder. To address this, we adopt contrastive feature learning in both self supervised and supervised learning frameworks, and show that these can lead to a significant increase in the prediction accuracy of a binary classifier on this task. We further validate this by conducting thorough experimental analyses under different set-ups on two publicly available datasets.

翻译：自闭症，亦称自闭症谱系障碍（ASD），是一种神经发育障碍。其主要症状包括（语言和/或非语言）沟通困难以及刻板/重复性行为。这些症状常与正常（对照）个体难以区分，导致该障碍在儿童早期未能确诊，从而延误治疗。由于幼儿期学习曲线陡峭，早期诊断自闭症可使患者及时获得适当干预措施，这对自闭症儿童的成长有积极影响。此外，传统自闭症诊断方法需多次就诊于专业精神科医生，这一过程耗时较长。本文提出一种基于学习的方法，利用受试者简短的动作视频片段实现自闭症自动化诊断。该任务极具挑战性，原因在于可用标注数据量少，且两类样本（ASD与对照组）间的差异通常难以辨别。这一点从基于基线编码器的交叉熵损失所训练的二分类器性能较差中也可得到验证。为解决此问题，我们在自监督与监督学习框架中引入对比特征学习，证明该方法能显著提升该任务中二分类器的预测准确率。我们进一步在两个公开数据集的不同实验设置下进行充分分析，验证了上述结论。