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医学成像中的基于深度学习的对象检测算法:系统审查

Carina Albuquerque1, Roberto Henriques1, Mauro Castelli1

  • 1NOVA Information Management School, Lisboa, Portugal.

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概括
此摘要是机器生成的。

医学图像中的深度学习对象检测正在迅速发展,在各种成像技术和解剖领域显示出巨大的潜力. 持续的研究对于优化这些强大的工具用于临床应用至关重要.

关键词:
图书统计学分析深度学习是一种深度学习.医学成像医学成像对象检测检测对象检测对象检测定性分析是一种定性分析.定量分析是一种量化分析.

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科学领域:

  • 计算机科学 计算机科学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 深度学习 (DL) 在各个领域取得了显著的进步,特别是在医疗图像分析中进行细分,检测和分类等任务.
  • 基于DL的医疗成像对象识别正在获得吸引力,因为它有可能提高诊断准确性和效率.

研究的目的:

  • 提供医疗图像中基于深度学习的对象识别的概述.
  • 探索DL物体检测的最新方法,成像技术和解剖应用.
  • 分析趋势并确定该领域的未来研究方向.

主要方法:

  • 使用PRISMA指南进行系统的文献审查.
  • 基于引用率的出版物的定量和定性分析.
  • 检查DL对象检测在不同成像模式和解剖学领域的利用情况.

主要成果:

  • 在医学成像中观察到基于DL的物体检测模型的使用量持续增加.
  • 研究在美国,中国和日本最活跃,主要是在医学和计算机科学领域.
  • DL方法具有适应性,应用于CR扫描,病理图像和内镜成像,在数字病理学和显微镜中具有显著的应用.

结论:

  • 深度学习对象检测在医学图像分析中显示了重要的,但尚未开发的潜力.
  • 不同的数据集大小,私人数据集的普及,以及前性研究的稀缺性都带来了挑战.
  • 持续的研究和特定应用的优化对于推动医疗成像中的DL至关重要.