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相关概念视频

Expected Value01:15

Expected Value

3.9K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
3.9K
Randomized Experiments01:13

Randomized Experiments

6.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.9K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Purposive Learning01:22

Purposive Learning

119
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...
119
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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预期最大化伪标签伪标签

Moucheng Xu1, Yukun Zhou2, Chen Jin3

  • 1UCL Centre for Medical Image Computing (CMIC), University College London, 90 High Holborn, London, WC1V 6LJ, UK; UCL Department of Medical Physics and Biomedical Engineering, University College London, Gower Street, London, WC1E 6BT, UK; Satsuma Lab, University College Londo, 90 High Holborn, WC1V 6LJ, UK.

Medical image analysis
|March 1, 2024
PubMed
概括
此摘要是机器生成的。

这项研究将伪标签与期望最大化联系起来,引入贝叶斯伪标签,以改善半监督医疗图像细分. 这种方法提高了模型的稳定性和准确性,在细分各种解剖结构.

关键词:
贝叶斯深度学习是贝叶斯的深度学习.预期最大化 预期最大化生成型模型是一种生成型模型.伪标签是一种伪标签.坚固性 坚固性分段化 分段化 分段化 分段化半监督学习 半监督学习

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Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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相关实验视频

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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科学领域:

  • 机器学习 机器学习
  • 医学图像分析 医学图像分析
  • 计算机视觉 计算机视觉

背景情况:

  • 伪标签是一种使用模型预测在未标签数据上的自我训练技术.
  • 它的经验成功是显而易见的,但缺乏强大的理论基础.
  • 现有的方法可能无法充分利用未标记的数据来进行可靠的模型训练.

研究的目的:

  • 建立伪标签和预期最大化 (EM) 算法之间的理论联系.
  • 为伪标签引入一个通用的框架,称为贝叶斯伪标签 (BPL).
  • 为了证明BPL在半监督的医疗图像细分任务中的有效性.

主要方法:

  • 将伪标签连接到EM算法,以了解其基本原则.
  • 使用贝叶斯定理来推导贝叶斯伪标签的伪标签的概括.
  • 开发一种具有适应性值的可变方法,以产生高质量的BPL.
  • 将伪标签和BPL应用于医疗图像 (CT,MRI) 的半监督细分.

主要成果:

  • 伪标签的经验成功是通过其与EM的联系来解释的.
  • 贝叶斯的伪标签提供了一个理论上有基础的概括.
  • 拟议的变化方法有效地产生了高质量的伪标签.
  • 在肺血管,脑瘤和前列腺MRI的半监督细分中显示出显著的改善.
  • 通过使用伪标签,证明了学习表达力的强化.

结论:

  • 伪标签是一种对更全面的基于EM的配方的经验估计.
  • 贝叶斯的伪标签提供了一个强大的和理论上健全的扩展.
  • 该方法显著提高了半监督医疗图像细分的准确性和稳定性.
  • 该框架适用于各种医学成像模式和细分任务.