Diagnostic performance of radiomic features from FS-T2WI for parotid tumor classification: A comparison of deep learning and machine learning models
- DOI
- 10.1016/j.jcms.2026.109891
- Published
- 2026-11
- Container
- Journal of Cranio-Maxillofacial Surgery
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jcms.2026.109891,
title = {Diagnostic performance of radiomic features from FS-T2WI for parotid tumor classification: A comparison of deep learning and machine learning models},
author = {Xuran Mao and Yanting Li and Wei Zhang and Huiming Yu and Xinghong Huang},
year = {2026},
journal = {Journal of Cranio-Maxillofacial Surgery},
doi = {10.1016/j.jcms.2026.109891},
url = {https://doi.org/10.1016/j.jcms.2026.109891}
}RIS
TY - JOUR TI - Diagnostic performance of radiomic features from FS-T2WI for parotid tumor classification: A comparison of deep learning and machine learning models AU - Xuran Mao AU - Yanting Li AU - Wei Zhang AU - Huiming Yu AU - Xinghong Huang PY - 2026 JO - Journal of Cranio-Maxillofacial Surgery DO - 10.1016/j.jcms.2026.109891 UR - https://doi.org/10.1016/j.jcms.2026.109891 ER -
APA
Mao, X., Li, Y., Zhang, W., Yu, H., & Huang, X. (2026). Diagnostic performance of radiomic features from FS-T2WI for parotid tumor classification: A comparison of deep learning and machine learning models. Journal of Cranio-Maxillofacial Surgery. https://doi.org/10.1016/j.jcms.2026.109891
Source records
- crossref · retrieved 2026-09-25T09:27:11.342Z