Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity Prediction.
- DOI
- 10.1109/tcsvt.2021.3063952
- Published
- 2022 May
- Container
- IEEE transactions on circuits and systems for video technology : a publication of the Circuits and Systems Society
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1109/tcsvt.2021.3063952,
title = {Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity Prediction.},
author = {Zhou J and Zhang X and Zhu Z and Lan X and Fu L and Wang H and Wen H},
year = {2022},
journal = {IEEE transactions on circuits and systems for video technology : a publication of the Circuits and Systems Society},
doi = {10.1109/tcsvt.2021.3063952},
url = {https://doi.org/10.1109/tcsvt.2021.3063952}
}RIS
TY - JOUR TI - Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity Prediction. AU - Zhou J AU - Zhang X AU - Zhu Z AU - Lan X AU - Fu L AU - Wang H AU - Wen H PY - 2022 JO - IEEE transactions on circuits and systems for video technology : a publication of the Circuits and Systems Society DO - 10.1109/tcsvt.2021.3063952 UR - https://doi.org/10.1109/tcsvt.2021.3063952 ER -
APA
J, Z., X, Z., Z, Z., X, L., L, F., H, W., & H, W. (2022). Cohesive Multi-Modality Feature Learning and Fusion for COVID-19 Patient Severity Prediction.. IEEE transactions on circuits and systems for video technology : a publication of the Circuits and Systems Society. https://doi.org/10.1109/tcsvt.2021.3063952
Source records
- pubmed · retrieved 2026-11-10T14:53:17.903Z