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相关概念视频

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Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
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Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
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相关实验视频

Updated: May 5, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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一个全面的AI模型开发框架,用于一致的格里森分级.

Xinmi Huo1, Kok Haur Ong1, Kah Weng Lau2,3

  • 1Computational Digital Pathology Lab, Bioinformatics Institute, A*STAR, Singapore, Singapore.

Communications medicine
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概括
此摘要是机器生成的。

格里森评分的人工智能 (AI) 是有前途的,但面临着挑战. 这种新的数字病理学工作流提高了不同扫描仪的AI准确性和效率,改善了病理学家的整合.

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科学领域:

  • 数字病理学数字病理学
  • 人工智能在医学中的应用
  • 计算病理学计算病理学

背景情况:

  • 基于人工智能的格里森分级显示了病理学家的潜力.
  • 挑战包括不一致的图像质量,数据整合需求和有限的概括性.
  • 这些限制阻碍了可扩展性和采用性.

研究的目的:

  • 为人工智能辅助的格里森分级开发一个全面的数字病理学工作流程.
  • 为了应对图像质量,数据集成和通用性的挑战.
  • 提高人工智能在格里森分级中的一致性,准确性和效率.

主要方法:

  • 一个数字病理学工作流程,包含图像质量控制 (A!MagQC) 和基于云的注释 (A!HistoClouds).
  • 病理学家-人工智能交互 (PAI) 为不断改进模型.
  • 用色彩增强和图像外观迁移技术来解决扫描器变化,通过对Akoya扫描图像的培训和对多个扫描器的评估.

主要成果:

  • 人工智能模型在Akoya扫描图像上获得了平均F1分数0.80和方位加权卡帕0.71的平均得分.
  • 一个通用化解决方案将格里森模式检测的平均F1分数从0.73提高到0.88在其他扫描仪的图像上.
  • 格里森的得分时间减少了43%,PAI的注释效率提高了2.5倍,提高了模型性能.

结论:

  • 开发的管道显著推进了人工智能辅助的格里森分级,提高了一致性,准确性和效率.
  • 该模型在各种扫描仪中展示了出色的性能,克服了以前扫描仪特定方法的局限性.
  • 这一进步促进了人工智能工具在临床病理学工作流程中无集成.