ArticleProceedings of SPIE--the International Society for Optical Engineering
Oral Cavity Anatomical Site Image Classification and Analysis.
Article in Proceedings of SPIE--the International Society for Optical Engineering. 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.
- Weakly Supervised Learning of Oral Cavity Images for Separating Precancer from Cancer.IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems · 2026Article
- Oral Cancer Detection By Using Tabular Data Synthesis and Classification.Proceedings ... ICDM workshops. IEEE International Conference on Data Mining · 2025Article
- Detecting Oral Cancer Using Tabular Deep Learning.IEEE International Conference on Omni-layer Intelligent Systems : COINS. IEEE International Conference on Omni-layer Intelligent Systems · 2025Article
- Cleaning and Harmonizing Medical Image Data for Reliable AI: Lessons Learned from Longitudinal Oral Cancer Natural History Study Data.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Extraction of Ruler Markings For Estimating Physical Size of Oral Lesions.Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition · 2022Article
- Automatic Detection of Oral Lesion Measurement Ruler Toward Computer-Aided Image-Based Oral Cancer Screening.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Oral cavity cancer is a common cancer that can result in breathing, swallowing, drinking, eating problems as well as speech impairment, and there is high mortality for the advanced stage. Its diagnosis is confirmed through histopathology. It is of critical importance to determine the need for biopsy and identify the correct location. Deep learning has demonstrated great promise/success in several image-based medical screening/diagnostic applications. However, automated visual evaluation of oral cavity lesions has received limited attention in the literature. Since the disease can occur in different parts of the oral cavity, a first step is to identify the images of different anatomical sites. We automatically generate labels for six sites which will help in lesion detection in a subsequent analytical module. We apply a recently proposed network called ResNeSt that incorporates channel-wise attention with multi-path representation and demonstrate high performance on the test set. The average F1-score for all classes and accuracy are both 0.96. Moreover, we provide a detailed discussion on class activation maps obtained from both correct and incorrect predictions to analyze algorithm behavior. The highlighted regions in the class activation maps generally correlate considerably well with the region of interest perceived and expected by expert human observers. The insights and knowledge gained from the analysis are helpful in not only algorithm improvement, but also aiding the development of the other key components in the process of computer assisted oral cancer screening.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.