X. Wen , C. Zhao , B. Zhao , M. Yuan , J. Chang , W. Liu , J. Meng , L. Shi , S. Yang , J. Zeng , Y. Yang
{"title":"Application of deep learning in radiation therapy for cancer","authors":"X. Wen , C. Zhao , B. Zhao , M. Yuan , J. Chang , W. Liu , J. Meng , L. Shi , S. Yang , J. Zeng , Y. Yang","doi":"10.1016/j.canrad.2023.07.015","DOIUrl":null,"url":null,"abstract":"<div><p>In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. This study summarizes the commonly used and latest deep learning algorithms (including transformer, and diffusion models), introduces the workflow of different radiotherapy, and illustrates the application of different algorithms in different radiotherapy modules, as well as the defects and challenges of deep learning in the field of radiotherapy, so as to provide some help for the development of automatic radiotherapy for cancer.</p></div>","PeriodicalId":9504,"journal":{"name":"Cancer Radiotherapie","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Radiotherapie","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S127832182400026X","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. This study summarizes the commonly used and latest deep learning algorithms (including transformer, and diffusion models), introduces the workflow of different radiotherapy, and illustrates the application of different algorithms in different radiotherapy modules, as well as the defects and challenges of deep learning in the field of radiotherapy, so as to provide some help for the development of automatic radiotherapy for cancer.
期刊介绍:
Cancer/radiothérapie se veut d''abord et avant tout un organe francophone de publication des travaux de recherche en radiothérapie. La revue a pour objectif de diffuser les informations majeures sur les travaux de recherche en cancérologie et tout ce qui touche de près ou de loin au traitement du cancer par les radiations : technologie, radiophysique, radiobiologie et radiothérapie clinique.