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相关概念视频

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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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Uncertainty: Overview00:59

Uncertainty: Overview

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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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相关实验视频

Updated: Sep 10, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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基于信心和不确定性的推断时间校正,以提高医学图像分类中的深度学习模型性能和可解释性

Joel Jeffrey1, Ashwin RajKumar1, Sudhanshu Pandey1

  • 1Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, 560012, India.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
|August 26, 2025
PubMed
概括

一个新的算法,基于信心和的不确定性值算法 (CEbUTAl),通过解决类不平衡和提高可解释性而改善人工智能 (AI) 医疗图像分析.

关键词:
相信自己的能力深度学习输入量可解释的人工智能可解释的人工智能不确定性

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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相关实验视频

Last Updated: Sep 10, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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科学领域:

  • 医学图像分析
  • 人工智能
  • 机器学习

背景情况:

  • 训练数据中的阶级不平衡和有限的解释性是医疗图像分析中人工智能的重大挑战.
  • 现有方法通常需要在模型性能和可解释性之间进行权衡.

研究的目的:

  • 引入新的后处理算法CEbUTAl,以提高医疗成像中的AI模型的性能和可解释性.
  • 解决阶级不平衡问题,提高临床环境中人工智能模型的可靠性.

主要方法:

  • 开发了基于信心和的不确定性值算法 (CEbUTAl) 作为一种模型不可知,任务不可知后处理技术.
  • 在各种深度学习架构和丢失函数中,CEbUTAl应用于五个医学成像任务,包括内出血检测和乳腺癌检测.

主要成果:

  • CEbUTAl提高了分类准确度约5%,并在多个任务和模型中提高了灵敏度.
  • 在解决阶级不平衡和量化不确定性方面超越了最先进的方法.
  • 证明增强可解释性不需要在人工智能模型性能上做出妥协.

结论:

  • CEbUTAl提供了一种可通用的方法来缓解类不平衡的偏见,并改善医学成像中的AI解释性.
  • 该算法提高了人工智能模型在临床实践中的实用性和可靠性.