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AJR Am J Roentgenol. 2020 Jul;215(1):192-197. doi: 10.2214/AJR.19.22346. Epub 2020 Apr 29.
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A patient-independent CT intensity matching method using conditional generative adversarial networks (cGAN) for single x-ray projection-based tumor localization.一种基于条件生成对抗网络(cGAN)的患者独立 CT 强度匹配方法,用于基于单 X 射线投影的肿瘤定位。
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深度学习辅助电子喉镜在喉白斑病诊断中的应用

[Application of deep learning assisted electronic laryngoscope in diagnosis of laryngeal leukoplakia].

作者信息

Fu Jia, Li Lijuan, Yan Yan, Ma Furong

出版信息

Lin Chuang Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2021 May;35(5):464-467. doi: 10.13201/j.issn.2096-7993.2021.05.019.

DOI:10.13201/j.issn.2096-7993.2021.05.019
PMID:34304477
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10128465/
Abstract

In recent years, medical imaging technology and computer technology have made great progress. On the one hand, with the development and popularization of electronic laryngoscope, the image of electronic laryngoscope plays a very important role in the diagnosis of vocal cord lesions. On the other hand, deep learning algorithm,especially convolutional neural networkhas gradually become the first choice of medical image recognition since the foundation of deep learning algorithm. So far, deep learning algorithm has made great contributions in many disciplines. In this paper, the basic concept of deep learning, the current status of image recognition of vocal cord lesions, and the prospect of research based on deep learning in vocal cord image lesions recognition are reviewed.

摘要

近年来,医学成像技术和计算机技术取得了巨大进展。一方面,随着电子喉镜的发展与普及,电子喉镜图像在声带病变诊断中发挥着非常重要的作用。另一方面,深度学习算法,尤其是卷积神经网络,自深度学习算法创立以来逐渐成为医学图像识别的首选。迄今为止,深度学习算法在许多学科中都做出了巨大贡献。本文对深度学习的基本概念、声带病变图像识别的现状以及基于深度学习在声带图像病变识别方面的研究前景进行了综述。