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Multiple Bar Graph01:07

Multiple Bar Graph

8.9K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
8.9K
Classification of Systems-II01:31

Classification of Systems-II

457
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,
457
Aggregates Classification01:29

Aggregates Classification

966
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...
966
Classification of Systems-I01:26

Classification of Systems-I

549
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:
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
42.8K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

389
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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相关实验视频

Updated: Jan 15, 2026

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

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CPGNet:多模式图形学习与层次类别指导多标签全幻灯片图像分类.

Haoyun Zhao, Dapeng Tao, Yibing Zhan

    IEEE journal of biomedical and health informatics
    |October 13, 2025
    PubMed
    概括

    新的多标签全幻灯片图像 (WSI) 分类器CPGNet解决了现实世界的癌症亚型挑战. 这种类别提示图形网络提高了数字病理学的诊断准确性.

    科学领域:

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

    背景情况:

    • 目前在全幻灯片图像 (WSI) 中的自动癌症类型识别使用单标签分类,这对于复杂的临床情景是不够的.
    • 现实世界的数字病理学数据通常具有多标签特征和类不平衡,挑战现有的自动化方法.
    • 准确的WSI分析对于癌症诊断,治疗计划和预后至关重要.

    研究的目的:

    • 开发一个先进的多标签全幻灯片图像 (WSI) 分类器,CPGNet,以更好地处理复杂的癌症亚型和数字病理学的类不平衡.
    • 通过整合本地和全球特征提取并利用语义类别关系来模仿病理学家的诊断过程.
    • 提高在临床环境中自动化癌症亚型识别的准确性和稳定性.

    主要方法:

    • CPGNet使用MaskSLIC对WSIs进行超像素细分,将其表示为带有节点和边缘的图形.
    • 一个带有多头自我注意机制 (GLGFI模块) 的图形神经网络 (GNN) 捕获了本地和全球空间依赖.
    • 视觉类型交互 (VCI) 模块利用语义关系,重权策略解决了类不平衡.

    主要成果:

    • 在私人 (YNLUAD) 和公共 (BCNB,AGGC22) 数据集中,CPGNet表现出卓越的性能,普遍性和稳定性.
    • 建议的多标签分类方法有效地处理现实世界癌症亚型识别的复杂性.

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    Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
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    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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  • 该模型成功地捕获了复杂的空间分布,并模仿了专家病理学家的诊断策略.
  • 结论:

    • 在数字病理学的自动化多标签WSI分类中,CPGNet提供了显著的进步.
    • 该模型处理阶级不平衡和复杂的空间关系的能力提高了其临床适用性.
    • 这种方法为癌症诊断和预后支持提供了更现实的和更有效的工具.