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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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相关实验视频

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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医学图像识别方法:一篇综述

Juan Li1, Pan Jiang2, Qing An3

  • 1School of Information Engineering, Wuhan Business University, Wuhan, 430056, China; School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China.

Computers in biology and medicine
|December 17, 2023
PubMed
概括

这篇论文回顾了先进的人工智能方法,例如用于医疗图像识别的深度学习. 它强调了它们在各种医学领域的诊断,细分和检测中的应用.

关键词:
分类 分类 分类 分类.深度学习是一种深度学习.医疗图像识别 医疗图像识别转移学习转移学习

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科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 计算机辅助诊断 计算机辅助诊断

背景情况:

  • 医疗图像识别对于计算机辅助诊断,检索和挖掘至关重要.
  • 智能成像对传统方法有优势,但由于不同的模式和病理,它面临着挑战.
  • 现有的方法分析电子健康记录和基因信息.

研究的目的:

  • 综合审查和总结最近关于医疗图像分析人工智能方法的研究.
  • 分析和讨论机器学习,深度学习和卷积神经网络在医学成像中的应用.
  • 提供当前进展,挑战和该领域未来研究方向的概述.

主要方法:

  • 审查最近应用机器学习,深度学习,卷积神经网络和转移学习的研究.
  • 分析用于医疗图像识别的图像处理技术.
  • 基于应用场景 (分类,细分,检测,注册) 和领域 (肺,大脑,数字病理等) 的方法分类. ) 的情况.

主要成果:

  • 深度学习和相关的人工智能方法在各种医疗图像分析任务中显示出重大前景.
  • 方法总结基于应用场景和特定的医疗领域,证明了广泛的适用性.
  • 该审查强调了不同人工智能技术的最新进展和贡献.

结论:

  • 人工智能,特别是深度学习,正在改变医学图像分析,为诊断和研究提供强大的工具.
  • 未来的研究应该专注于解决开放的挑战和探索新的应用程序,潜在地整合计算机视觉和自然语言处理.
  • 人工智能算法的持续进步将进一步提高医疗图像识别和解释的能力.