{"title":"用随机掩蔽比掩盖潜伏变压器提高氟斑牙的诊断","authors":"Hao Xu , Yun Wu , Junpeng Wu , Rui Xie , Maohua Gu , Rongpin Wang","doi":"10.1016/j.jvcir.2025.104496","DOIUrl":null,"url":null,"abstract":"<div><div>Dental fluorosis is a chronic condition caused by long-term overconsumption of fluoride, which leads to changes in the appearance of tooth enamel. Diagnosing its severity can be challenging for dental professionals, and limited research on deep learning applications in this field. Therefore, we propose a novel deep learning model, masked latent transformer with random masking ratio (MLTrMR), to advance the diagnosis of dental fluorosis. MLTrMR enhances contextual learning by using a masked latent modeling scheme based on Vision Transformer. It extracts latent tokens from the original image with a latent embedder, processes unmasked tokens with a latent transformer (LT) block, and predicts masked tokens. To improve model performance, we incorporate an auxiliary loss function. MLTrMR achieves state-of-the-art results, with 80.19% accuracy, 75.79% F1 score, and 81.28% quadratic weighted kappa on the first open-source dental fluorosis image dataset (DFID) we constructed. The dataset and code are available at <span><span>https://github.com/uxhao-o/MLTrMR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"111 ","pages":"Article 104496"},"PeriodicalIF":3.1000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Masked latent transformer with random masking ratio to advance the diagnosis of dental fluorosis\",\"authors\":\"Hao Xu , Yun Wu , Junpeng Wu , Rui Xie , Maohua Gu , Rongpin Wang\",\"doi\":\"10.1016/j.jvcir.2025.104496\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Dental fluorosis is a chronic condition caused by long-term overconsumption of fluoride, which leads to changes in the appearance of tooth enamel. Diagnosing its severity can be challenging for dental professionals, and limited research on deep learning applications in this field. Therefore, we propose a novel deep learning model, masked latent transformer with random masking ratio (MLTrMR), to advance the diagnosis of dental fluorosis. MLTrMR enhances contextual learning by using a masked latent modeling scheme based on Vision Transformer. It extracts latent tokens from the original image with a latent embedder, processes unmasked tokens with a latent transformer (LT) block, and predicts masked tokens. To improve model performance, we incorporate an auxiliary loss function. MLTrMR achieves state-of-the-art results, with 80.19% accuracy, 75.79% F1 score, and 81.28% quadratic weighted kappa on the first open-source dental fluorosis image dataset (DFID) we constructed. The dataset and code are available at <span><span>https://github.com/uxhao-o/MLTrMR</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":54755,\"journal\":{\"name\":\"Journal of Visual Communication and Image Representation\",\"volume\":\"111 \",\"pages\":\"Article 104496\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Visual Communication and Image Representation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1047320325001105\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Visual Communication and Image Representation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1047320325001105","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Masked latent transformer with random masking ratio to advance the diagnosis of dental fluorosis
Dental fluorosis is a chronic condition caused by long-term overconsumption of fluoride, which leads to changes in the appearance of tooth enamel. Diagnosing its severity can be challenging for dental professionals, and limited research on deep learning applications in this field. Therefore, we propose a novel deep learning model, masked latent transformer with random masking ratio (MLTrMR), to advance the diagnosis of dental fluorosis. MLTrMR enhances contextual learning by using a masked latent modeling scheme based on Vision Transformer. It extracts latent tokens from the original image with a latent embedder, processes unmasked tokens with a latent transformer (LT) block, and predicts masked tokens. To improve model performance, we incorporate an auxiliary loss function. MLTrMR achieves state-of-the-art results, with 80.19% accuracy, 75.79% F1 score, and 81.28% quadratic weighted kappa on the first open-source dental fluorosis image dataset (DFID) we constructed. The dataset and code are available at https://github.com/uxhao-o/MLTrMR.
期刊介绍:
The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.