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相关概念视频

Aggregates Classification01:29

Aggregates Classification

953
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
953
Classification of Systems-II01:31

Classification of Systems-II

446
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
446
Classification of Systems-I01:26

Classification of Systems-I

540
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
540

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相关实验视频

Updated: Jan 9, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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超越准确性:量化多个实例学习对整个幻灯片图像分类的可靠性.

Hassan Keshvarikhojasteh1, Marc Aubreville2, Christof A Bertram3

  • 1Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

PloS one
|December 5, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型在病理学中的可靠性至关重要. 平均聚合实例 (MEAN-POOL-INS) 模型显示了全幻灯片图像分类的卓越可靠性,提供了可靠的基线.

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相关实验视频

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科学领域:

  • 计算病理学计算病理学
  • 机器学习 机器学习
  • 医学成像分析分析 医学成像分析

背景情况:

  • 机器学习 (ML) 模型被广泛使用,但其可靠性令人担忧.
  • 整个幻灯片图像 (WSI) 分类的多个实例学习 (MIL) 模型缺乏可靠性评估.
  • 这种差距阻碍了它们在临床决策中的使用.

研究的目的:

  • 引入量化指标来评估MIL模型的可靠性.
  • 在病理学数据集上评估常见MIL架构的可靠性.
  • 为了确定可靠的MIL模型用于WSI分类.

主要方法:

  • 开发了用于可靠性评估的三种新的定量指标.
  • 应用于多个MIL架构 (例如,MEAN-POOL-INS) 的指标.
  • 为了评估,利用了三个区域性注释病理学数据集.

主要成果:

  • 平均聚合实例 (MEAN-POOL-INS) 模型表现出卓越的可靠性.
  • 尽管设计简单且效率高,但MEAN-POOL-INS表现出高可靠性.
  • 在不同的MIL架构和数据集中,可靠性有所不同.

结论:

  • 可靠性评估对于计算病理学的MIL模型至关重要.
  • MEAN-POOL-INS作为WSI分类的可靠和高效的基准.
  • 这些发现支持可靠的MIL模型的临床适用性.