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相关概念视频

The Uncertainty Principle04:08

The Uncertainty Principle

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Uncertainty in Measurement: Reading Instruments02:46

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty in Measurement: Significant Figures03:34

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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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Uncertainty: Confidence Intervals00:54

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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在IVIM MRI中对Voxel-Wise监督深度学习模型的不确定性量化进行全面的框架.

Nicola Casali1,2, Alessandro Brusaferri1, Giuseppe Baselli1,2

  • 1Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato, Consiglio Nazionale delle Ricerche, Milan, Italy.

NMR in biomedicine
|January 20, 2026
PubMed
概括

这项研究引入了一个深度学习框架,使用混合密度网络的深层集合,以在MRI中更准确地估计intravoxel不连贯运动 (IVIM) 参数. 该方法量化了不确定性,提高了扩散MRI分析的可靠性.

关键词:
一种不确定性的不确定性.校准校准的时间一个深层合奏.认识体系的不确定性.在intravoxel不连贯的运动.磁共振成像技术的使用混合密度网络 混合密度网络

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科学领域:

  • 医学成像和物理学的医学成像和物理
  • 机器学习在医学诊断中的应用
  • 量化MRI技术 量化MRI技术

背景情况:

  • 从扩散权重MRI中精确估计intravoxel不连贯运动 (IVIM) 参数,由于错误的反向问题和噪声敏感性,这是一个挑战.
  • 现有的方法在与噪声作斗争,特别是在 perfusion 区间,限制了 IVIM 参数量化的可靠性.
  • 不确定性量化对于解释扩散MRI中估计参数的可靠性至关重要.

研究的目的:

  • 开发和评估一个概率深度学习框架,以进行可靠的IVIM参数估计.
  • 为了使预测不确定性能够被量化和分解成 aleatoric (AU) 和 epistemic (EU) 组件.
  • 将拟议的框架与现有的非概率和概率方法进行基准测试.

主要方法:

  • 开发了一个概率深度学习框架,利用混合密度网络 (MDNs) 的深度集合 (DE).
  • 对合成数据进行监督训练,对模拟和体内小鼠大脑MRI数据集进行评估.
  • 使用校准曲线,预测分布度和CRPS评估了不确定性量化可靠性.

主要成果:

  • 与其他方法相比,MDNs对扩散系数 (D) 和 perfusion 分数 (f) 的预测分布进行了更高的校准和更清晰的预测.
  • 对于伪扩散系数 (D*) 发现了轻微的过度自信,但MDNs在体内D*估计得到了更平稳的结果.
  • 活体内高高的认识不确定性 (EU) 表明了与实际收购条件的潜在不匹配,突出了DE的价值.

结论:

  • 拟议的深层组合MDN框架为IVIM配件提供了全面的不确定性量化,识别了不可靠的估计.
  • 这种方法提高了扩散MRI参数估计的可靠性和可解释性.
  • 该框架可适应其他物理模型,并进行适当的调整.