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相关概念视频

Mean Absolute Deviation01:13

Mean Absolute Deviation

2.6K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
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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...
6.3K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.3K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.3K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.6K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
7.6K
Weighted Mean00:57

Weighted Mean

5.0K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.0K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K

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Neuronavigated Focalized Transcranial Direct Current Stimulation Administered During Functional Magnetic Resonance Imaging
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基于先前平均偏差的人工神经网络对IVIM参数进行映射.

Guodong Hu1, Chen Ye1, Ming Zhong2

  • 1Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China.

Medical physics
|September 6, 2024
PubMed
概括

这项研究介绍了IterANN,这是一个新的框架,通过解决分布之外的问题来改进intravoxel不连贯运动 (IVIM) 成像参数估计. IterANN提高了准确性和稳定性,以便更好地诊断疾病.

关键词:
第四十一章 没有了人工神经网络的人工神经网络完全监督的 完全监督的已经从分销中退出.

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科学领域:

  • 医疗成像医学成像
  • 生物医学工程 生物医学工程
  • 机器学习 机器学习

背景情况:

  • 内素不连贯运动 (IVIM) 成像为疾病管理提供了有前途的生物标志物.
  • 用于IVIM参数估计的监督学习方法由于分布外 (OOD) 问题而遭受性能恶化.
  • 模拟和现实世界IVIM数据集之间存在差距,阻碍了临床应用.

研究的目的:

  • 提出IterANN,一个新的学习框架,以克服IVIM参数估计中的OOD问题.
  • 为了提高IVIM参数估计的准确性和稳定性,使用先前平均偏差 (MDP).
  • 提高IVIM成像用于疾病诊断的临床适用性.

主要方法:

  • IterANN使用一个简单的人工神经网络 (ANN),用于多b值信号的输入层和三个IVIM参数的输出层.
  • 该框架包含一个先前的平均偏差 (MDP),以代地更新模拟训练数据的分布.
  • 调整训练数据分布以使其平均值与预测的真实数据值保持一致,从而增强相关性.

主要成果:

  • 在模拟数据集上的IVIM参数中,IterANN实现了最小的残余误差,特别是在低SNR时.
  • 该方法表明,与次优方法相比,对关键IVIM参数的剩余误差有显著的减少.
  • 在真实数据集上,IterANN产生了最高的参数对比度和噪声比率 (PCNR),以及具有较低变化系数 (CV) 的优越稳定性.

结论:

  • 使用MDP更新培训数据分布有效地解决了IVIM分析中的OOD问题.
  • IterANN显著提高了估计的IVIM参数的准确性和稳定性.
  • IterANN的增强性能增加了IVIM成像用于疾病诊断的潜力.