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相关概念视频

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Unusual Results01:16

Unusual Results

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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
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Regression Toward the Mean01:52

Regression Toward the Mean

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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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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Outliers and Influential Points01:08

Outliers and Influential Points

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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相关实验视频

Updated: Jul 4, 2025

Cross-Modal Multivariate Pattern Analysis
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Cross-Modal Multivariate Pattern Analysis

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在无监督异常检测中的 (可预测) 性能偏差.

Felix Meissen1, Svenja Breuer2, Moritz Knolle3

  • 1Chair for AI in Healthcare and Medicine, Klinikum rechts der Isar der Technischen Universität München, Einsteinstr. 25, Munich, 81675, Germany.

EBioMedicine
|February 9, 2024
PubMed
概括

无监督异常检测 (UAD) 模型显示,即使有均衡的数据,人口学子组的表现也存在差异. 新的"公平法则"揭示了子组代表和模型性能之间的线性关系,指导数据集构成,用于医疗成像中的公平人工智能.

关键词:
算法偏差是一种算法偏差.异常检测检测异常检测人工智能的人工智能是人工智能.机器学习是机器学习.小组的差异 亚组的差异

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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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相关实验视频

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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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科学领域:

  • 医学成像人工智能 医学成像人工智能
  • 医疗保健中的算法公平性
  • 机器学习用于疾病检测和检测

背景情况:

  • 越来越多的医学成像数据需要临床医生使用人工智能工具.
  • 无监督异常检测 (UAD) 模型对于早期疾病识别至关重要.
  • 在UAD模型中的公平性仍然是一个未经探索的领域,与监督模型不同.

研究的目的:

  • 调查数据集组成如何影响跨人口亚组的UAD模型性能.
  • 使用胸部X射线数据集量化UAD模型中的公平差异.
  • 在机器学习模型中引入一个衡量公平性的指标.

主要方法:

  • 在三个大型公共胸部X射线数据集上评估了UAD模型的性能.
  • 评估基于数据集子组表示的受保护变量之间的性能变化.
  • 利用了两个最先进的UAD模型,并引入了小组AUROC (sAUROC) 进行公平量化.

主要成果:

  • 发现了经验上的"公平法则",证明了子组代表性和异常检测性能之间的线性关系.
  • 即使使用平衡的培训数据,也观察到绩效差异.
  • 识别了复合效应,使多个代表性不足的群体中的个体的表现恶化.

结论:

  • 在人口分组中量化了UAD模型的差异性表现.
  • 证明平衡的代表性本身并不能减轻不公平;一些子组对模型来说更难学习.
  • 发现的"公平性规律"使得不同表现的估计成为可能,并为公平的人工智能指导最佳数据集组成.