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Stress triggers a coordinated physiological response involving the sympathetic nervous system (SNS) and the hypothalamic-pituitary-adrenal (HPA) axis. This dual activation ensures that the body is prepared for both immediate and prolonged stress management. The process begins with the perception of a stressor. This initial phase activates the SNS, leading to the rapid release of adrenaline (epinephrine) from the adrenal glands.
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According to George Herbert Mead, as children progress beyond the game stage, they develop a more comprehensive understanding of societal rules and norms. This cognitive and social development enables them to internalize the expectations of the broader community, refining their ability to regulate behavior.Consistent participation in organized activities is crucial in helping children recognize that their actions are not isolated but contribute to a more significant, interconnected group...
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细胞ViT++:使用基础模型进行节能和适应性细胞细分和分类.

Fabian Hörst1, Moritz Rempe1, Helmut Becker2

  • 1Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, 45131, Germany; Cancer Research Center Cologne Essen (CCCE), West German Cancer Center Essen, University Hospital Essen (AöR), Essen, 45131, Germany; Department of Physics, TU Dortmund University, Dortmund, 44227, Germany.

Computer methods and programs in biomedicine
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概括

细胞ViT++提供了一个数据效率高的深度学习框架,用于数字病理学中的细胞细分. 这种轻量级的模型可以快速适应新的细胞类型,使用最小的数据,从而降低计算成本和注释时间.

关键词:
人工智能的人工智能是人工智能.细胞 细胞 细胞数字病理学数字病理学基金会模型 基金会模型分段化 分段化 分段化 分段化

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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
  • 机器学习用于医学成像.

背景情况:

  • 对于细胞细分的深度学习模型需要大量的注释数据,并且在计算上昂贵.
  • 现有的方法缺乏适应新细胞类型的适应性,在研究和临床工作流程中造成瓶.
  • 引入CellViT++是为了解决细胞细分和分类中的这些局限性.

研究的目的:

  • 开发一个数据效率高和轻量级的框架,用于一般化的细胞细分.
  • 以最少的数据,使模型能够快速适应新的细胞分类学.
  • 为了降低计算成本和对数字病理学专家注释的依赖.

主要方法:

  • 细胞ViT ++ 使用视觉变压器与冷预训练的基础模型进行细分.
  • 深层细胞嵌入在前进传递过程中被提取,没有额外的计算成本.
  • 在这些嵌入式上训练了一个轻量级分类器,以适应新的细胞类型,并展示了一个自动化工作流程,用于从H&E和IF幻灯片生成训练数据.

主要成果:

  • 细胞ViT++实现了显著的零射击细分和数据效率,在公共数据集上表现优于竞争方法.
  • 使用CoNSeP数据集中仅10%的培训数据获得了优异的结果.
  • 分类器方法大大减少了培训时间 (分钟与小时) 和二氧化碳排放量96.93%,在自动标签上训练的模型显示了与专家注释的数据集相比的或更高的性能.

结论:

  • CellViT++是一个强大的,高效的,开源的框架,将细分从计算病理学分类中解开.
  • 该框架能够适应使用最小数据和自动数据集生成的新细胞类型,这大大减少了对专家注释的需求.
  • 细胞ViT ++ 作为一个基础工具,加速研究,增强诊断,并使更深入的队列分析.