SguiNet: Stimulus-guided depression detection network based on ocular images

Depression is a common and serious mental disorder, and the ocular image is often utilized in early intervention and adjunctive treatment for depression due to its direct association with cognitive processes. Despite recent advancements in ocular movement detection for depression, exploration of enhancing depression detection through simulated visual cognitive processing using ocular movement patterns induced by emotional stimuli remains limited. Deep learning algorithms can enable non-contact and cost-effective early detection of depression by extracting subtle changes in ocular images. In this work, we introduce a novel stimulus-guided depression detection method (SguiNet) that enhances the mining of specific ocular movement patterns through visual processing modalities. The framework employs semantic pseudo-labels of emotional stimulus images to assist in accurately capturing the affective polarity exhibited by depressed and non-depressed participants during emotional stimulation, thereby enabling effective depression detection. First, an Adaptive Temporal Pyramid Module (ATPM) is proposed for extracting high-level spatio-temporal features from ocular video data. Subsequently, a Time Scale Cross Attention Module (TSCAM) is employed to enhance the feature extraction capability of the model. Finally, the model is jointly trained with depression classification based on the generated semantic pseudo-labels and real labels by the Stimulus Paradigm Supervision Module (SPSM). We utilize data collected from a study on computer-aided diagnosis, where participants' ocular movements were recorded while they viewed stimulus images. The results demonstrate that the proposed SguiNet method effectively models ocular movement features with discriminative power for depression detection and provides new insights into ocular movement pattern-assisted depression detection. On a dataset collected from three medical institutions, SguiNet achieves an accuracy of 82.99%, a precision of 80.74%, and an F1-score of 83.85%, highlighting its superior detection capability.