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相关概念视频

Aggregates Classification01:29

Aggregates Classification

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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...
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Classification of Systems-II01:31

Classification of Systems-II

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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,
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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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.
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社区注意力转换器多个实例学习整个幻灯片图像分类.

Rukhma Aftab1, Qiang Yan1,2, Juanjuan Zhao1

  • 1College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, Shanxi, China.

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概括

社区注意力转换器多实例学习 (NATMIL) 通过分析背景来改善从整个幻灯片图像中进行癌症诊断. 这种监督较弱的方法提高了分类瘤的准确性,优于现有的方法.

关键词:
注意力变压器注意力变压器肺癌是一种肺癌.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.整个幻灯片图像的图像.

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科学领域:

  • 计算病理学计算病理学
  • 医学中的人工智能
  • 数字病理学数字病理学

背景情况:

  • 病理学家使用全幻灯片图像 (WSIs) 进行癌症诊断,但深度学习模型与瘤异质性作斗争.
  • 由于局部分析,分类WSI的弱监督模型可能会产生虚假阳性/负值.

研究的目的:

  • 引入NATMIL (邻里注意力转换器多阶段学习) 以改进基于WSI的癌症分类.
  • 为了利用WSI之间的上下文依赖性,以便更准确地进行瘤亚型化.

主要方法:

  • 开发了NATMIL,结合了邻里注意力变压器来整合背景.
  • 通过在WSIs中考虑更广泛的组织背景来增强多个实例的学习.

主要成果:

  • 纳米尔在亚型非小细胞肺癌 (NSCLC) 和淋巴结 (LN) 瘤中表现出卓越的准确性.
  • 在Camelyon数据集上达到89.6%的准确性,在TCGA-LUSC数据集上达到88.1%.
  • 在定量分析中表现优于现有的监管较弱的算法.

结论:

  • 通过减少孤立分析中的错误,NATMIL显著提高了瘤分类的准确性.
  • 整合上下文依赖性可以提高使用WSIs进行癌症诊断的准确性.
  • NATMIL显示出作为数字病理学应用程序的强大工具的潜力.