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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Associative Learning01:27

Associative Learning

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...
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
Observational Learning01:12

Observational Learning

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 because...

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相关实验视频

Updated: May 14, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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使用基于注意力的多重实例学习量化和分子特征化.

Francesco Cisternino1, Yipei Song2,3, Tim S Peters4

  • 1Human Technopole, Viale Rita Levi-Montalcini 1, 20157, Milan, Italy.

medRxiv : the preprint server for health sciences
|March 17, 2025
PubMed
概括

动脉硬性斑块内血块 (IPH) 检测是使用机器学习自动化,改善心血管事件预测. 这种数字病理学方法准确量化IPH,揭示斑块不稳定性和主要心血管不良事件的分子驱动因素.

关键词:
动脉样硬化是一种动脉样硬化.甲板内出血 甲板内出血心血管疾病心血管疾病遗传学 遗传学 遗传学 是一个机器学习是机器学习.多个实例的学习学习多个实例的学习.

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科学领域:

  • 心血管病理学心血管病理学
  • 数字病理学数字病理学
  • 机器学习在医学中的应用

背景情况:

  • 内血块出血 (IPH) 是动脉样硬化斑块脆弱性和心血管不良事件的关键指标.
  • 在组织学图像中手动量化IPH是主观的,容易发生观察者之间的变化.
  • 准确的IPH评估对于理解斑块不稳定性和预测患者的结果至关重要.

研究的目的:

  • 开发和验证基于机器学习的自动化框架,用于在动脉样硬化斑块中检测和量化IPH.
  • 为了比较IPH量化不同组织学染料的性能.
  • 将数字病理与分子数据相结合,以描述IPH及其与临床结果的关联.

主要方法:

  • 一个基于注意力的附加多重实例学习 (MIL) 框架是使用来自Athero-Express生物库 (2595名患者) 的全幻灯片图像开发的.
  • 九种不同的组织学染色,包括血素和乙 (H&E),被评估用于IPH检测.
  • 研究了组合模型,将H&E与CD68或Verhoeff-Van Gieson (EVG) 弹性纤维染色相结合.
  • IPH区域来自MIL衍生的注意力得分,并使用单细胞转录学分析分子途径.

主要成果:

  • 开发的MIL框架准确地检测和量化IPH,优于手动评分.
  • 血素和欧 (H&E) 染色显示出高性能 (AUROC = 0.86),通过结合CD68或EVG (AUROC = 0.92) 显著改善.
  • IPH的存在和区域被确定为术前症状和主要不良心血管事件 (MACE) 的最强预测因素.
  • 确定了与IPH相关的关键分子通路,包括TNF-α信号传递和泡细胞存在.

结论:

  • 使用数字病理学和机器学习的自动IPH量化提供了一个可扩展,可复制和可解释的斑块表型化方法.
  • 这种方法提高了心血管事件的预测,并为IPH驱动的斑块不稳定性背后的分子机制提供了新的见解.
  • 这些发现有助于更深入地了解IPH如何导致症状和MACE,为改善患者管理铺平了道路.