Dynamic Summary Generation for Interpretable Multimodal Depression Detection

• URL: http://arxiv.org/abs/2604.11334v1
• Date: Mon, 13 Apr 2026 11:34:40 GMT
• Title: Dynamic Summary Generation for Interpretable Multimodal Depression Detection
• Abstract summary: Depression remains widely underdiagnosed and undertreated because stigma and subjective symptom ratings hinder reliable screening.<n>We propose a coarse-to-fine, multi-stage framework that leverages large language models for accurate and interpretable detection.
• Score: 23.358673852385337
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Depression remains widely underdiagnosed and undertreated because stigma and subjective symptom ratings hinder reliable screening. To address this challenge, we propose a coarse-to-fine, multi-stage framework that leverages large language models (LLMs) for accurate and interpretable detection. The pipeline performs binary screening, five-class severity classification, and continuous regression. At each stage, an LLM produces progressively richer clinical summaries that guide a multimodal fusion module integrating text, audio, and video features, yielding predictions with transparent rationale. The system then consolidates all summaries into a concise, human-readable assessment report. Experiments on the E-DAIC and CMDC datasets show significant improvements over state-of-the-art baselines in both accuracy and interpretability.

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