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，又称自闭症谱系障碍（ASD），是一种神经发育性疾病。其主要症状包括（言语和非言语）沟通困难以及刻板/重复性行为。这些症状往往难以与正常（对照）个体区分，导致该疾病在儿童早期未能得到诊断，从而延误治疗。由于婴幼儿时期学习曲线陡峭，早期诊断自闭症能够及时采取适当干预措施，这可能对自闭症儿童的成长产生积极影响。此外，传统自闭症诊断方法需要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，利用受试者的简短动作视频片段实现自闭症的自动化诊断。该任务极具挑战性，原因在于可用的标注数据量少，且两个类别（ASD与对照组）的样本间差异通常难以辨认。这一点从基于基线编码器使用交叉熵损失训练的二元分类器性能欠佳中也可得到印证。为解决此问题，我们在自监督学习和监督学习框架中采用对比特征学习，并证明这些方法能显著提升该任务中二元分类器的预测准确率。通过两个公开数据集上不同设置下的详尽实验分析，我们进一步验证了该方法的有效性。