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相关概念视频

Aggregates Classification01:29

Aggregates Classification

350
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...
350
Classification of Systems-II01:31

Classification of Systems-II

183
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,
183
Associative Learning01:27

Associative Learning

461
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.
Classical conditioning, also known...
461
Observational Learning01:12

Observational Learning

225
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
225
Classification of Systems-I01:26

Classification of Systems-I

222
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:
222
Introduction to Learning01:18

Introduction to Learning

479
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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相关实验视频

Updated: Jul 26, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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代的多个实例学习对于弱注释的整个幻灯片图像分类.

Yuanpin Zhou1, Shuanlong Che2, Fang Lu2

  • 1School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, People's Republic of China.

Physics in medicine and biology
|June 13, 2023
PubMed
概括
此摘要是机器生成的。

一种新的代多重实例学习 (MIL) 方法改善了全幻灯片图像 (WSI) 在基因病理学中的分类. 这种方法通过改进图像补丁的特征提取来提高诸如肺癌等疾病的诊断准确性.

关键词:
注意力机制注意力机制组织病理学 组织病理学多个实例的学习学习多个实例的学习.自主监督学习学习整个幻灯片图像 整体幻灯片图像

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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
  • 数字组织病理学 数字组织病理学
  • 机器学习在医学中的应用

背景情况:

  • 整个幻灯片图像 (WSIs) 对组织病理学至关重要,但它们的高分辨率使详细的注释变得复杂.
  • 将WSIs分类通常使用多个实例学习 (MIL),将WSI视为一个实例 (补丁) 的袋子.

研究的目的:

  • 开发一种新的代多个实例学习 (IMIL) 方法,仅使用幻灯片级标签来对世界互联网进行分类.
  • 通过改进WSI分类来提高组织病理学分析的准确性.

主要方法:

  • 提出了一种代的MIL (IMIL) 方法,可以协同学习实例和袋子表示.
  • 使用基于注意力的MIL聚合的选择实例和伪标签,实现了特征提取器的代微调.
  • 雇员自主监督学习用于特征提取器初始化,基于注意力得分的样本选择,以及对强有力的培训的自信意识损失.

主要成果:

  • 在Camelyon16和KingMed-Lung数据集上,IMIL-SimCLR实现了最佳的分类性能,平均AUC高达4.25%超过了CLAM基线.
  • 在TCGA-肺部数据集上,IMIL-ImageNet表现出优异的性能,AUC为96.55%和精度为96.76%,超过CLAM的AUC为1.65%和精度为2.09%.
  • 在各种WSI分类任务中,IMIL方法在公共和内部数据集中被证明有效.

结论:

  • 拟议的代MIL (IMIL) 方法为组织病理学WSI分类提供了重大进展.
  • 与最先进的MIL方法相比,IMIL在多个数据集和分类任务中表现出卓越的性能.
  • 这种方法有望提高数字病理学的诊断准确性和效率.