Unified VideoMAE Framework for Detection of Multi-Disorder ADHD and Depression

Mental disorders have become a major public health issue worldwide. Traditional face-to-face and clinical diagnostic methods are not only time-consuming and expensive but also rely heavily on the expertise of professionals and require costly equipment. With the rapid development of computer vision and deep learning, a growing number of studies are exploring deep learning methods to assist in diagnosing and evaluating various mental disorders. However, most current research focuses on a single type of mental disorder, which limits the broader application of these technologies. This paper proposes a novel system aimed at detecting multiple mental disorders, i.e., Attention Deficit Hyperactivity Disorder (ADHD) and depression, within one unified framework. To make the proposed system both computationally efficient and robust despite the limited availability of mental health datasets, we first preprocess the original videos to extract cost-effective facial video segments and then fine-tune pre-trained models on these facial videos for classification. In our mental disorder detection task, three distinct fine-tuning methods are adopted for the pre-trained video-masked autoencoder (VideoMAE) model. Moreover, unlike conventional fine-tuning procedures, the proposed methods innovatively refine the attention masks during fine-tuning to prioritise facial features most relevant to mental disorders. Experimental results show that our proposed method outperforms existing multi-mental disorder detection approaches on standard benchmarks, achieving competitive accuracy with fewer parameters, stable cross-dataset performance, and improved robustness. This work points to a promising direction for automated mental disorder diagnostic technologies.