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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

535
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.
535
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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: 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 in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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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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Updated: Jun 22, 2025

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
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不确定性意识多实例学习可靠的分类:应用到光学连贯性断层扫描的应用.

Coen de Vente1, Bram van Ginneken2, Carel B Hoyng3

  • 1Quantitative Healthcare Analysis (QurAI) Group, Informatics Institute, University of Amsterdam, Amsterdam, Noord-Holland, Netherlands; Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Noord-Holland, Netherlands; Diagnostic Image Analysis Group (DIAG), Department of Radiology and Nuclear Medicine, Radboudumc, Nijmegen, Gelderland, Netherlands.

Medical image analysis
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概括
此摘要是机器生成的。

由于文物,深度学习模型与来自不同扫描仪的医疗图像作斗争. 基于不确定性的实例排除 (UBIX) 通过排除文物损坏的数据来提高可靠性,从而提高了AI模型在各种数据集中的适用性.

关键词:
可以概括的概括性可以解释性 解释性光学连贯性断层扫描技术.在分销之外的检测检测

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科学领域:

  • 医疗图像分析 医学图像分析
  • 人工智能的人工智能是人工智能.
  • 计算机视觉 计算机视觉 计算机视觉

背景情况:

  • 医疗成像的深度学习模型通常在不同于用于训练的扫描仪的数据上表现较差.
  • 医疗扫描中的供应商特定的文物是这种普遍性差的主要原因.
  • 没有再培训,现有的模型可能无法在各种临床环境中可靠地执行.

研究的目的:

  • 引入和评估基于不确定性的实例排除 (UBIX),这是一种新的方法,可以提高深度学习分类模型的可靠性.
  • 提高医疗图像分析模型对不同供应商和扫描仪数据的通用性.
  • 为应对影响光学连贯性断层扫描 (OCT) 中模型性能的特定供应商工件的挑战,以确定与年龄相关的黄斑变性阶段.

主要方法:

  • UBIX是一个推断时间模块,旨在用于多实例学习 (MIL) 设置.
  • 它通过不确定性估计来检测和减少文物损坏实例 (例如2D切片) 对整体袋级预测 (例如体积图像) 的贡献.
  • 分布外 (OOD) 检测用于识别有未见的工件的实例.

主要成果:

  • 当应用到具有供应商特定文物的外部数据集时,UBIX表现出可靠的性能,二次加权kappa (κw) 从0.861降至0.708.8略有下降.
  • 一个没有UBIX的最先进的3D神经网络在相同的外部数据集上经历了显著的性能下降 (κw从0.852到0.084).
  • 乌比克斯成功识别了有未见文物的实例,减少了它们对预测的影响,而不需要重新训练模型.

结论:

  • 在医学图像分析中,UBIX有效地提高了深度学习模型的可靠性和通用性,特别是在扫描器特定文物存在的情况下.
  • 该方法在应用于不同供应商的数据时提高了模型的稳定性,减轻了性能恶化.
  • 在不需要广泛的再培训的情况下,UBIX为提高人工智能在各种临床环境中的实用性提供了一个有希望的解决方案.