{"title":"[Trans-YOLOv5:基于 YOLOv5 的先验变压器网络模型,用于自动检测宫颈细胞学图像中的异常细胞或团块]。","authors":"Wenran Hu, Rong Fu","doi":"10.12122/j.issn.1673-4254.2024.07.01","DOIUrl":null,"url":null,"abstract":"<p><p>The development of various models for automated images screening has significantly enhanced the efficiency and accuracy of cervical cytology image analysis. Single-stage target detection models are capable of fast detection of abnormalities in cervical cytology, but an accurate diagnosis of abnormal cells not only relies on identification of a single cell itself, but also involves the comparison with the surrounding cells. Herein we present the Trans-YOLOv5 model, an automated abnormal cell detection model based on the YOLOv5 model incorporating the global-local attention mechanism to allow efficient multiclassification detection of abnormal cells in cervical cytology images. The experimental results using a large cervical cytology image dataset demonstrated the efficiency and accuracy of this model in comparison with the state-of-the-art methods, with a mAP reaching 65.9% and an AR reaching 53.3%, showing a great potential of this model in automated cervical cancer screening based on cervical cytology images.</p>","PeriodicalId":18962,"journal":{"name":"Nan fang yi ke da xue xue bao = Journal of Southern Medical University","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11270655/pdf/","citationCount":"0","resultStr":"{\"title\":\"[Trans-YOLOv5: a YOLOv5-based prior transformer network model for automated detection of abnormal cells or clumps in cervical cytology images].\",\"authors\":\"Wenran Hu, Rong Fu\",\"doi\":\"10.12122/j.issn.1673-4254.2024.07.01\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The development of various models for automated images screening has significantly enhanced the efficiency and accuracy of cervical cytology image analysis. Single-stage target detection models are capable of fast detection of abnormalities in cervical cytology, but an accurate diagnosis of abnormal cells not only relies on identification of a single cell itself, but also involves the comparison with the surrounding cells. Herein we present the Trans-YOLOv5 model, an automated abnormal cell detection model based on the YOLOv5 model incorporating the global-local attention mechanism to allow efficient multiclassification detection of abnormal cells in cervical cytology images. The experimental results using a large cervical cytology image dataset demonstrated the efficiency and accuracy of this model in comparison with the state-of-the-art methods, with a mAP reaching 65.9% and an AR reaching 53.3%, showing a great potential of this model in automated cervical cancer screening based on cervical cytology images.</p>\",\"PeriodicalId\":18962,\"journal\":{\"name\":\"Nan fang yi ke da xue xue bao = Journal of Southern Medical University\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11270655/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nan fang yi ke da xue xue bao = Journal of Southern Medical University\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12122/j.issn.1673-4254.2024.07.01\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nan fang yi ke da xue xue bao = Journal of Southern Medical University","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12122/j.issn.1673-4254.2024.07.01","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
[Trans-YOLOv5: a YOLOv5-based prior transformer network model for automated detection of abnormal cells or clumps in cervical cytology images].
The development of various models for automated images screening has significantly enhanced the efficiency and accuracy of cervical cytology image analysis. Single-stage target detection models are capable of fast detection of abnormalities in cervical cytology, but an accurate diagnosis of abnormal cells not only relies on identification of a single cell itself, but also involves the comparison with the surrounding cells. Herein we present the Trans-YOLOv5 model, an automated abnormal cell detection model based on the YOLOv5 model incorporating the global-local attention mechanism to allow efficient multiclassification detection of abnormal cells in cervical cytology images. The experimental results using a large cervical cytology image dataset demonstrated the efficiency and accuracy of this model in comparison with the state-of-the-art methods, with a mAP reaching 65.9% and an AR reaching 53.3%, showing a great potential of this model in automated cervical cancer screening based on cervical cytology images.