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    此摘要是机器生成的。

    视觉转换器正在通过对整个幻灯片图像进行先进的分析来实现癌症诊断和治疗,从而彻底改变了计算病理学. 本综述探讨了它们在分类,细分和生存预测中的应用.

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    科学领域:

    • 计算型他的病理学
    • 医学中的人工智能.
    • 数字病理学数字病理学

    背景情况:

    • 深度学习主导着用于分析整个幻灯片图像的计算病理学.
    • 转换器,利用自我注意力,正在成为一个关键的深度学习进步.

    研究的目的:

    • 为了提供一个全面的综述最先进的视觉变换器在他的病理图像分析.
    • 讨论癌症诊断,预后和治疗建议中的应用.
    • 概述这一领域的视觉转换者面临的挑战和未来前景.

    主要方法:

    • 视觉转换器的基本概念和组件的概述.
    • 讨论最近的应用,包括整个幻灯片图像的分类和细分.
    • 使用定制的变压器架构探索生存风险回归.

    主要成果:

    • 视觉变压器在分析复杂的组织病理学数据方面显示出显著的潜力.
    • 针对特定任务,如分类和细分,正在开发量身定制的架构.
    • 应用范围扩展到预测患者生存结果.

    结论:

    • 视觉转换器提供了一种强大的方法来增强计算病理学.
    • 对挑战和新型架构的进一步研究对于推进癌症患者护理至关重要.
    • 这一综述是为开发先进的诊断和治疗技术提供准则.