The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
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
Citations
Cites
1 retracted
Retracted: Diagnostic Criteria for Depression based on Both Static and Dynamic Visual Features
Pandya, D. D., Jadeja, A., Degadwala, S., & Vyas, D. (2023) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot56793.2023.10053450 · Cited by 37
RetractedDeep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Self-citationVaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111
CNN architectures for large-scale audio classification
Hershey, S., Chaudhuri, S., Ellis, D. P. W., Gemmeke, J. F., Jansen, A., Moore, R. C., Plakal, M., Platt, D., Saurous, R. A., Seybold, B., et al. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952132 · Cited by 2,733
Research works printed but not linked: 6
- Joshi, 2013, Multimodal assistive technologies for depression diagnosis and monitoring, J. Multimodal User Interfaces, 7, 217, 10.1007/s12193-013-0123-2 · DOI
- X. Ma, H. Yang, Q. Chen, D. Huang, Y. Wang, Depaudionet: An efficient deep model for audio based depression classification, in: Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge, 2016, pp. 35–42. · DOI
- Yang, 2018, Integrating deep and shallow models for multi-modal depression analysis—hybrid architectures, IEEE Trans. Affect. Comput., 12, 239, 10.1109/TAFFC.2018.2870398 · DOI
- Lang, 2018, Automated depression analysis using convolutional neural network from speech, J. Biomed. Inform., 103, 111
- Niu, 2023, Multimodal spatiotemporal representation for automatic depression level detection, IEEE Trans. Affect. Comput., 14, 294, 10.1109/TAFFC.2020.3031345 · DOI
- Jan, 2017, Artificial intelligent system for automatic depression level analysis through visual and vocal expressions, IEEE Trans. Cogn. Dev. Syst., 10, 668, 10.1109/TCDS.2017.2721552 · DOI · Paper page
Cited by
Sorted by citations
Large language models in medical and healthcare fields: applications, advances, and challenges
Wang, D., & Zhang, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10921-0 · Cited by 246
Self-citationA Review of Key Technologies for Emotion Analysis Using Multimodal Information
Zhu, X., Guo, C., Feng, H., Huang, Y., Feng, Y., Wang, X., & Wang, R. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10287-z · Cited by 143
A systematic survey of air quality prediction based on deep learning
Zhang, Z., Zhang, S., Chen, C., & Yuan, J. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.03.031 · Cited by 138
Self-citationOnciul, R., Tataru, C.-I., Dumitru, A. V., Crivoi, C., Serban, M., Covache-Busuioc, R.-A., Radoi, M. P., & Toader, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020550 · Cited by 113
Fang, J., Yang, L., Wen, X., Yu, H., Li, W., Adamowski, J., & Barzegar, R. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131275 · Cited by 62
Self-citation
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
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