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Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Decision Making01:20

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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Creating and executing a nursing diagnosis helps nurses plan care and guide patient, family, and community interventions. They are developed based on a patient's physical evaluation and support measuring the outcomes. It is not recommended to select random interventions throughout the planning process. Instead, consider the following six essential factors when choosing interventions:
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Coping strategies are methods people use to manage, tolerate, or reduce the effects of stressors. These strategies involve both behavioral and psychological actions to handle stressful situations. One common approach is problem-focused coping, which aims to change or eliminate the source of stress rather than merely addressing its consequences. This method involves taking direct action to resolve the issue causing stress.
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积极学习使用可适应的基于任务的优先级.

Shaheer U Saeed1, João Ramalhinho1, Mark Pinnock1

  • 1Centre for Medical Image Computing, Wellcome/EPSRC Centre for Interventional & Surgical Sciences, and Department of Medical Physics & Biomedical Engineering, University College London, London, UK.

Medical image analysis
|April 19, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个人工智能控制器,用于在主动学习中对医疗图像进行优先排序,大大减少了对专家注释的需求. 该方法适应新任务,提高细分精度,使用更少的标记数据.

关键词:
积极学习是积极学习.医疗图像质量 医疗图像质量分段化 分段化 分段化 分段化

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科学领域:

  • 医疗图像计算 医疗图像计算
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 用于医学成像的监督机器学习需要广泛的专家注释,这耗时且昂贵.
  • 没有标记的医疗图像数据通常是丰富的,为高效的学习策略提供了机会.

研究的目的:

  • 开发一种可适应的积极学习策略,用于标签效率高的医疗图像细分.
  • 创建一个控制器神经网络,在批量模式的积极学习中对图像进行专家注释的优先级.

主要方法:

  • 开发了一个控制器神经网络,用于测量批次内的图像优先级,用于多类细分.
  • 控制器使用马尔科夫决策过程 (MDP) 框架内的超强化学习算法进行了优化.
  • 该方法使用来自超过一千名患者的CT数据集在九个腹部器官细分任务中进行了验证.

主要成果:

  • 拟议的可适应优先级度量实现了对新细分任务的融合细分精度,使用标签比启发式或随机方法少40-60%.
  • 观察到显著的性能改善:与随机优先排序相比,脏细分的子得分为22.6%,肝血管细分为10.2%.
  • 控制器展示了跨机构和跨机构的适应性,有效地优先考虑图像用于新的细分任务.

结论:

  • 开发的meta-reinforcement学习方法使一个可适应的优先级控制器能够实现高效的医疗图像注释.
  • 这种方法大大减少了训练准确的医疗图像细分模型所需的标记数据量.
  • 可适应的优先级策略显示出强大的潜力,可以提高机器学习在临床环境中的效率和有效性.