Depression and anxiety characterization and detection with multimodal deep learning

The role of multimodal deep learning in diagnosing depression and anxiety is highlighted, utilizing diverse data sources and key methods include convolutional, recurrent and graph neural networks, addressing challenges in data fusion, feature extraction and model interpretability for enhanced clinical relevance.