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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.11334 |
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| _version_ | 1866918442802610176 |
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| author | Teng, Shiyu Liu, Jiaqing Sun, Hao Li, Yu Chai, Shurong Hou, Ruibo Tateyama, Tomoko Lin, Lanfen Chen, Yen-Wei |
| author_facet | Teng, Shiyu Liu, Jiaqing Sun, Hao Li, Yu Chai, Shurong Hou, Ruibo Tateyama, Tomoko Lin, Lanfen Chen, Yen-Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11334 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamic Summary Generation for Interpretable Multimodal Depression Detection Teng, Shiyu Liu, Jiaqing Sun, Hao Li, Yu Chai, Shurong Hou, Ruibo Tateyama, Tomoko Lin, Lanfen Chen, Yen-Wei Artificial Intelligence 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. |
| title | Dynamic Summary Generation for Interpretable Multimodal Depression Detection |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.11334 |