HSILDNet: A Novel Deep Learning-based Method for Cervical Lesions Detection-Reference-Cited by-同舟云学术

HSILDNet: A Novel Deep Learning-based Method for Cervical Lesions Detection

Published:2022-11-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 9th International Conference on Biomedical and Bioinformatics Engineering
language:
Short-container-title:

Author:

Cao Yuzhen¹^ORCID,Ma Huizhan¹^ORCID,Fan Yinuo²^ORCID,Liu Yuzhen³^ORCID,Wang Shuo¹^ORCID,Yu Hui⁴^ORCID

Affiliation:

1. School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, China

2. Academy of Medical Engineering and Translational Medicine, Tianjin University, China

3. Department of Obstetrics and Gynecology, Affiliated Hospital of Weifang Medical University, China

4. School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, China and Academy of Medical Engineering and Translational Medicine, Tianjin University, China

Funder

Tianjin Municipal Science and Technology Bureau

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3574198.3574204

Reference20 articles.

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3. World Health Organization . WHO guideline for screening and treatment of cervical pre-cancer lesions for cervical cancer prevention , second edition. 2021 . World Health Organization. WHO guideline for screening and treatment of cervical pre-cancer lesions for cervical cancer prevention, second edition. 2021.

4. Kurman RJ , Carcangiu ML , Herrington CS , WHO classification of tumours of female reproductive organs . 4 th Eds. IARC press , Lyon , 2014 :172-176. Kurman RJ, Carcangiu ML, Herrington CS, WHO classification of tumours of female reproductive organs. 4th Eds. IARC press, Lyon, 2014:172-176.

5. Multimodal Entity Coreference for Cervical Dysplasia Diagnosis