Application of deep learning in radiation therapy for cancer

IF 1.5 4区 医学 Q4 ONCOLOGY
X. Wen , C. Zhao , B. Zhao , M. Yuan , J. Chang , W. Liu , J. Meng , L. Shi , S. Yang , J. Zeng , Y. Yang
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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.

深度学习在癌症放射治疗中的应用。
近年来,随着人工智能的发展,深度学习逐渐被应用到临床治疗和研究中。在癌症治疗的重要方法--放射治疗中,它也找到了自己的应用方向。本研究总结了常用和最新的深度学习算法(包括变换器、扩散模型等),介绍了不同放疗的工作流程,阐述了不同算法在不同放疗模块中的应用,以及深度学习在放疗领域的缺陷和挑战,以期为癌症自动放疗的发展提供一些帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Radiotherapie
Cancer Radiotherapie 医学-核医学
CiteScore
2.20
自引率
23.10%
发文量
129
审稿时长
63 days
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.
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