ArticleScientific reports2022
Effective deep learning for oral exfoliative cytology classification.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Deep learning single-cell analysis for cytologic evaluation of oral potentially malignant disorders.Scientific reports · 2026Article
- Detection of maxillary sinus pathologies using deep learning algorithms.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Training high-performance deep learning classifier for diagnosis in oral cytology using diverse annotations.Scientific reports · 2024Article
- Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists.Scientific reports · 2023Article
- Deep Learning-Based Screening of Urothelial Carcinoma in Whole Slide Images of Liquid-Based Cytology Urine Specimens.Cancers · 2022Article
- AI performance in oral cytology for differentiating poorly defined tumor cells from reactive atypia.Journal of oral biology and craniofacial researchArticle
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11 authors.
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Abstract
The use of sharpness aware minimization (SAM) as an optimizer that achieves high performance for convolutional neural networks (CNNs) is attracting attention in various fields of deep learning. We used deep learning to perform classification diagnosis in oral exfoliative cytology and to analyze performance, using SAM as an optimization algorithm to improve classification accuracy. The whole image of the oral exfoliation cytology slide was cut into tiles and labeled by an oral pathologist. CNN was VGG16, and stochastic gradient descent (SGD) and SAM were used as optimizers. Each was analyzed with and without a learning rate scheduler in 300 epochs. The performance metrics used were accuracy, precision, recall, specificity, F1 score, AUC, and statistical and effect size. All optimizers performed better with the rate scheduler. In particular, the SAM effect size had high accuracy (11.2) and AUC (11.0). SAM had the best performance of all models with a learning rate scheduler. (AUC = 0.9328) SAM tended to suppress overfitting compared to SGD. In oral exfoliation cytology classification, CNNs using SAM rate scheduler showed the highest classification performance. These results suggest that SAM can play an important role in primary screening of the oral cytological diagnostic environment.
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