Lumin Xing

Abstract Automated assessment through the analysis of facial expressions in autism can assist in early screening, providing strong support for timely intervention and contributing to the healthy development of patients. However, current deep learning models still face several challenges in the practical application of intelligent diagnosis for Autism Spectrum Disorder (ASD). First, traditional models struggle to fully capture subtle facial expression changes and local fine-grained features.

Dry Eye Disease (DED) is one of the most common eye diseases[1] and a primary cause of ocular surface dysfunction and inflammation[2]. 5%-30% of the world's population and more than 15 million Americans suffer from DED [3]–​[5]. 25% of patients who come to ophthalmic clinics suffer from DED[6], [7]. The severity of DED significantly varies [8]–​[11]. Due to the complicated etiology[1], the prevalence and recurrence rates are incredibly high[2], [5].