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概括
此摘要是机器生成的。

人工智能 (AI) 和数字病理学正在彻底改变医疗保健. 本综述详细介绍了自2018年以来机器学习在组织病理图像分析方面的进步,并解决了关键挑战和未来趋势.

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计算机辅助诊断是一种计算机辅助诊断.深度学习是一种深度学习.数字图像分析数字图像分析基金会模型 基金会模型组织病理学 组织病理学机器学习 机器学习整个幻灯片图像的图像.

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科学领域:

  • 计算病理学计算病理学
  • 数字病理学数字病理学
  • 组织病理学 组织病理学

背景情况:

  • 人工智能 (AI) 和数字病理学代表了医疗保健和生物医学研究的范式转变.
  • 本综述是对2018年出版物的更新,重点是机器学习 (ML) 在基因病学图像分析中的应用.
  • 在扩大计算病理学的技术能力和实际应用方面取得了重大进展.

研究的目的:

  • 提供机器学习应用在组织病理图像分析中的全面分析,重点关注自2018年以来的发展.
  • 突出解决关键挑战的进展,如处理千兆像素整片图像,数据稀缺,多维分析,域移动和模型可解释性等.
  • 评估正在重塑该领域的基础模型和多式联运一体化等新兴趋势.

主要方法:

  • 关于数字病理学中的机器学习应用的综合文献综述.
  • 分析解决特定技术和实际挑战的进展情况.
  • 评估新兴趋势及其对现场的影响.

主要成果:

  • 在处理大规模遗传病理图像 (千兆像素整片图像) 方面取得了显著进展.
  • 克服缺乏标记数据和跨机构域名转移的方法已经得到了改进.
  • 新兴趋势,如基础模型和多式联运一体化,对未来的应用有很大的前景.
  • 在病理学中提高机器学习模型的可解释性是一个正在进行的开发领域.

结论:

  • 机器学习具有很大的潜力,可以增强常规的病理分析,加速科学发现.
  • 计算病理学领域正在迅速发展,受到人工智能创新的推动.
  • 本综述为研究人员和临床医生提供有关病理图像分析现状和未来方向的指导.