A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning

Ruqian Hao, Lin Liu, Jing Zhang, Xiangzhou Wang, Juanxiu Liu, Xiaohui Du, Wen He, Jicheng Liao, Lu Liu, Yuanying Mao

DOI: 10.1155/2022/1929371
Published: 2022-02-27
Container: Journal of Healthcare Engineering
Publisher: Wiley
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1155/2022/1929371,
  title = {A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning},
  author = {Ruqian Hao and Lin Liu and Jing Zhang and Xiangzhou Wang and Juanxiu Liu and Xiaohui Du and Wen He and Jicheng Liao and Lu Liu and Yuanying Mao},
  year = {2022},
  journal = {Journal of Healthcare Engineering},
  doi = {10.1155/2022/1929371},
  url = {https://doi.org/10.1155/2022/1929371}
}

RIS

TY  - JOUR
TI  - A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning
AU  - Ruqian Hao
AU  - Lin Liu
AU  - Jing Zhang
AU  - Xiangzhou Wang
AU  - Juanxiu Liu
AU  - Xiaohui Du
AU  - Wen He
AU  - Jicheng Liao
AU  - Lu Liu
AU  - Yuanying Mao
PY  - 2022
JO  - Journal of Healthcare Engineering
DO  - 10.1155/2022/1929371
UR  - https://doi.org/10.1155/2022/1929371
ER  -

APA

Hao, R., Liu, L., Zhang, J., Wang, X., Liu, J., Du, X., He, W., Liao, J., Liu, L., & Mao, Y. (2022). A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning. Journal of Healthcare Engineering. https://doi.org/10.1155/2022/1929371

Source records

crossref · retrieved 2026-09-25T01:28:17.102Z