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Transformer-based multimodal feature enhancement networks for multimodal depression detection integrating video, audio and remote photoplethysmograph signals
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“LLMMs can also be trained to recognize and analyze medical images (e.g., X-rays, magnetic resonance imaging, and ultrasound), video, audio and remote photoplethysmograph signals to identify features and structures (Fan et al. 2024), aiding doctors in accurately and rapidly detecting anomalies and diagnosing diseases or injuries, thereby alleviating the workload of radiologists (Waisberg et al. 202”
From Large language models in medical and healthcare fields: applications, advances, and challenges (2024) · cites this paper as Fan et al. 2024
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Metadata
- Authors
- Fan, H., Zhang, X., Xu, Y., Fang, J., Zhang, S., Zhao, X., & Yu, J.
- Affiliations
- Zhejiang Sci-Tech University (CN)
- Taizhou University (CN)
- Hangzhou Dianzi University (CN)
- Corresponding author
- Zhang, Xingnan
- Zhang, Shiqing
- Zhao, Xiaoming
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2024-04
- Volume
- 104
- Pages
- 102161
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W4389195633
- Cited by
- 137
- References
- 94
- Citations by year
- 2026: 40 · 2025: 63 · 2024: 34
- FWCI
- 35.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Fusion (publishedVersion)
- Funders
- National Natural Science Foundation of China (61836002, 61976149, 62125201, 62276180)
- Natural Science Foundation of Zhejiang Province (62101375, LQ21F020001, LQ21F020002, LZ20F020002)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- depression detection, multimodal fusion, remote photoplethysmography, video features, audio features, emotion recognition
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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