MF$^{2}$-Net: Exploring a Meta-Fuzzy Multimodal Fusion Network for Depression Recognition

Depression is a prevalent mental illness that significantly impacts the well-being of individuals and the development of society. The current diagnostic methods are largely subjective and time-consuming. Moreover, the existing machine learning-based depression recognition methods struggle to fully exploit the collaborative benefits between modalities, lack interpretability in their fusion processes, and perform inadequately in few-shot depression recognition tasks. To address these challenges, we propose a meta-fuzzy multimodal fusion network (MF$^{2}$-Net) for depression recognition. This innovative approach integrates physiological signals and behavioral data, employs multiple multilayer perceptrons (MLPs) to learn the fuzzy measures of single base learners and complementary increments, then constructs all fuzzy measures, and finally achieves an interpretable decision-level fusion process through fuzzy integrals. Furthermore, we incorporate model-agnostic meta-learning for conducting few-shot domain-adaptive training, mitigating the issues related to high individual variability levels and the scarcity of multimodal depression data. Our method demonstrates exceptional classification performance in subject-independent experiments implemented on public datasets; offers a reliable solution for objectively, effectively, and conveniently recognizing depression; and has the potential to promote the clinical applications of rapid intelligent depression diagnosis.