{"title":"Vision transformer architecture and applications in digital health: a tutorial and survey.","authors":"Khalid Al-Hammuri, Fayez Gebali, Awos Kanan, Ilamparithi Thirumarai Chelvan","doi":"10.1186/s42492-023-00140-9","DOIUrl":null,"url":null,"abstract":"<p><p>The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that plays an important role in digital health applications. Medical images account for 90% of the data in digital medicine applications. This article discusses the core foundations of the ViT architecture and its digital health applications. These applications include image segmentation, classification, detection, prediction, reconstruction, synthesis, and telehealth such as report generation and security. This article also presents a roadmap for implementing the ViT in digital health systems and discusses its limitations and challenges.</p>","PeriodicalId":52384,"journal":{"name":"Visual Computing for Industry, Biomedicine, and Art","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333157/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visual Computing for Industry, Biomedicine, and Art","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1186/s42492-023-00140-9","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Arts and Humanities","Score":null,"Total":0}
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Abstract
The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that plays an important role in digital health applications. Medical images account for 90% of the data in digital medicine applications. This article discusses the core foundations of the ViT architecture and its digital health applications. These applications include image segmentation, classification, detection, prediction, reconstruction, synthesis, and telehealth such as report generation and security. This article also presents a roadmap for implementing the ViT in digital health systems and discusses its limitations and challenges.
视觉转换器（ViT）是一种用于图像识别任务的先进架构，在数字医疗应用中发挥着重要作用。医疗图像占数字医疗应用数据的 90%。本文将讨论 ViT 架构的核心基础及其数字医疗应用。这些应用包括图像分割、分类、检测、预测、重建、合成以及远程医疗（如报告生成和安全）。本文还介绍了在数字医疗系统中实施 ViT 的路线图，并讨论了其局限性和挑战。
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