ID | 65744 |
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Sukegawa, Shintaro
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
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Ono, Sawako
Department of Pathology, Kagawa Prefectural Central Hospital
Tanaka, Futa
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Inoue, Yuta
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Hara, Takeshi
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Yoshii, Kazumasa
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Nakano, Keisuke
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
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Takabatake, Kiyofumi
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
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Kawai, Hotaka
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
Katsumitsu, Shimada
Department of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental University
Nakai, Fumi
Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine
Nakai, Yasuhiro
Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine
Miyazaki, Ryo
Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine
Murakami, Satoshi
Department of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental University
Nagatsuka, Hitoshi
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
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Miyake, Minoru
Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine
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Abstract | The study aims to identify histological classifiers from histopathological images of oral squamous cell carcinoma using convolutional neural network (CNN) deep learning models and shows how the results can improve diagnosis. Histopathological samples of oral squamous cell carcinoma were prepared by oral pathologists. Images were divided into tiles on a virtual slide, and labels (squamous cell carcinoma, normal, and others) were applied. VGG16 and ResNet50 with the optimizers stochastic gradient descent with momentum and spectral angle mapper (SAM) were used, with and without a learning rate scheduler. The conditions for achieving good CNN performances were identified by examining performance metrics. We used ROCAUC to statistically evaluate diagnostic performance improvement of six oral pathologists using the results from the selected CNN model for assisted diagnosis. VGG16 with SAM showed the best performance, with accuracy = 0.8622 and AUC = 0.9602. The diagnostic performances of the oral pathologists statistically significantly improved when the diagnostic results of the deep learning model were used as supplementary diagnoses (p-value = 0.031). By considering the learning results of deep learning model classifiers, the diagnostic accuracy of pathologists can be improved. This study contributes to the application of highly reliable deep learning models for oral pathological diagnosis.
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Note | The version of record of this article, first published in Scientific Reports, is available online at Publisher’s website: http://dx.doi.org/10.1038/s41598-023-38343-y
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Published Date | 2023-07-19
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Publication Title |
Scientific Reports
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Volume | volume13
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Issue | issue1
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Publisher | Nature Portfolio
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Start Page | 11676
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ISSN | 2045-2322
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Content Type |
Journal Article
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language |
English
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OAI-PMH Set |
岡山大学
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Copyright Holders | © The Author(s) 2023
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File Version | publisher
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Related Url | isVersionOf https://doi.org/10.1038/s41598-023-38343-y
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License | http://creativecommons.org/licenses/by/4.0/
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Citation | Sukegawa, S., Ono, S., Tanaka, F. et al. Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists. Sci Rep 13, 11676 (2023). https://doi.org/10.1038/s41598-023-38343-y
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Funder Name |
Japan Society for the Promotion of Science
Japan Science and Technology Agency
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助成番号 | JP19K19158
JPMJCR21D4
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