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Self-Awareness and Its Effects01:21

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Self-awareness is a psychological state in which the individual becomes the focal point of their attention. This inward focus transforms the self into an object of contemplation and assessment, influencing how individuals perceive their actions and their alignment with personal and societal standards.Triggers and Contexts for Self-AwarenessSelf-awareness can be activated by external stimuli that make individuals visually or audibly aware of themselves, such as mirrors, cameras, or recordings.
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排名意识的多个实例学习对组织病理学幻灯片分类:开发和验证研究研究.

Ho Heon Kim1,2, Gisu Hwang1, Won Chan Jeong1

  • 1AI R&D Center, Seegene Medical Foundation, Seoul, Republic of Korea.

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概括
此摘要是机器生成的。

排名诱导,一种新的多实例学习 (MIL) 框架,有效地使用部分专家注释来改善数字病理学的幻灯片级分类. 这种方法在具有有限或粗略注释的现实场景中表现出稳健性.

关键词:
数据效率培训数据效率培训数字病理学数字病理学学习如何排名的排名.混合监管 混合监管是指混合监管.多个实例的学习学习多个实例的学习.缺乏监督的学习学习.整个幻灯片图像 整体幻灯片图像

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科学领域:

  • 数字病理学数字病理学
  • 计算病理学计算病理学
  • 机器学习在医学中的应用

背景情况:

  • 多重实例学习 (MIL) 是数字病理学的幻灯片级分类的一个关键技术.
  • 当前的MIL方法往往不能有效地利用部分专家注释.
  • 专家的注释,即使是部分的,也可以显著提高监督学习模型.

研究的目的:

  • 开发和评估一个排名意识的MIL框架,名为排名诱导.
  • 将部分专家注释集成到MIL中,以改善幻灯片级分类.
  • 在现实的注释约束下评估框架的性能.

主要方法:

  • 开发了排名诱导,一种利用对对排名损失的MIL方法,灵感来自Rank.Net.
  • 该框架通过更重视注释区域来优先考虑诊断相关的贴片.
  • 在各种注释场景下对Camelyon16,DigestPath2019和SMF-stomach数据集进行评估.

主要成果:

  • 排名引入获得了高的AUROC分数:0.839 (Camelyon16),0.995 (DigestPath2019) 和0.875 (SMF-stomach). 在这个过程中,我们可以获得高的AUROC分数.
  • 该模型在低数据模式中表现出稳定性,在训练数据减少的情况下保持0.761 AUROC.
  • 只有20%的稀疏的幻灯片级注释才能达到接近和的性能.

结论:

  • 通过基于排名的监督整合专家注释,可以提高基于MIL的分类性能.
  • 排名诱导证明在数字病理学应用中具有有限,粗略或稀疏注释的实用性和稳健性.