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相关概念视频

The Uncertainty Principle04:08

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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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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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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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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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Updated: Jan 30, 2026

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DUCore:双不确定性引导一致性和半监督医疗图像细分区域对比学习.

Maregu Assefa, Muzammal Naseer, Kumie Gedamu

    IEEE journal of biomedical and health informatics
    |January 28, 2026
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    概括

    本研究介绍了DUCore,这是一种用于半监督医疗图像细分的新框架. 它通过适应性优先考虑不确定的区域和完善特征可分离性来提高模型的稳定性和精确性,从而在划分复杂结构时提高模型的稳定性和精确性.

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    科学领域:

    • 医疗成像医学成像
    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 半监督学习对于医学图像细分至关重要,但现有的不确定性估计方法增加了计算成本,可能会丢弃有价值的数据.
    • 目前的方法往往错过了复杂的结构,如模两可的损伤边界,因为丢弃了不确定的区域.

    研究的目的:

    • 引入双不确定性引导一致性和区域对比学习 (DUCore) 框架,以改善医疗图像细分.
    • 解决现有的不确定性估计方法在计算成本和数据处理方面的局限性.

    主要方法:

    • DUCore集成了双不确定性导向的一致性损失 (DuCL) 和区域对比损失 (ReCL).
    • DuCL使用确定性单通不确定性估计 (基于的aleatoric,代理迪里克莱特的认识) 和权重不确定区域.
    • ReCL采用基于边界和梯度的硬负采矿,以提高特征的分离性.

    主要成果:

    • 通过自适应性校准预测对齐,DUCore 提高了细分的稳定性.
    • 该框架有效地以更高的精度划分精细结构和复杂的边界.
    • 实验表明,DUCore在医疗细分基准上的表现优于现有的基于一致性的方法.

    结论:

    • 在医疗图像细分中,DUCore提供了一种更有效和有效的方法来学习不确定性意识的一致性.
    • 该方法通过加权,而不是丢弃不确定的区域来保存有价值的学习信号.
    • 在处理复杂结构和模两可的边界方面,DUCore表现出卓越的性能.