Training high-performance deep learning classifier for diagnosis in oral cytology using diverse annotations-Reference-Cited by-同舟云学术

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Training high-performance deep learning classifier for diagnosis in oral cytology using diverse annotations

Published:2024-07-30 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Sukegawa Shintaro,Tanaka Futa,Nakano Keisuke,Hara Takeshi,Ochiai Takanaga,Shimada Katsumitsu,Inoue Yuta,Taki Yoshihiro,Nakai Fumi,Nakai Yasuhiro,Ishihama Takanori,Miyazaki Ryo,Murakami Satoshi,Nagatsuka Hitoshi,Miyake Minoru

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-67879-w.pdf

Reference17 articles.

1. Warnakulasuriya, S. et al. An appraisal of oral cancer and pre-cancer screening programmes in Europe: A systematic review. J. Oral Pathol. Med 44, 559–570 (2015).

2. Sekine, J., Nakatani, E., Hideshima, K., Iwahashi, T. & Sasaki, H. Diagnostic accuracy of oral cancer cytology in a pilot study. Diagn. Pathol. 12, (2017).

3. Sukegawa, S. et al. Clinical study on primary screening of oral cancer and precancerous lesions by oral cytology. Diagn. Pathol. 15, (2020).

4. Sukegawa, S. et al. Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists. Sci. Rep. 13, (2023).

5. Sukegawa, S. et al. Effective deep learning for oral exfoliative cytology classification. Sci. Rep. 12, (2022).

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