Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

990
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.
990
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.1K
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...
1.1K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.8K
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...
4.8K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

893
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...
893
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Survival Tree01:19

Survival Tree

166
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...
166

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality.

IEEE transactions on medical imaging·2026
Same author

ET-Pfam: ensemble transfer learning for protein family prediction.

Bioinformatics (Oxford, England)·2026
Same author

ChronoRoot 2.0: an open AI-powered platform for 2D temporal plant phenotyping.

GigaScience·2026
Same author

GNN2Pfam: Integrating protein sequence and structure with graph neural networks for Pfam domain annotation.

Journal of structural biology·2026
Same author

Predicting cardiovascular disease risk using retinal optical coherence tomography imaging.

Frontiers in artificial intelligence·2025

相关实验视频

Updated: Sep 16, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.4K

在域转移下实现可靠的WMH细分:使用最大调整的应用研究,以改善不确定性估计.

Franco Matzkin1, Agostina Larrazabal2, Diego H Milone1

  • 1Institute for Signals, Systems and Computational Intelligence, sinc(i) CONICET-UNL, Santa Fe, Argentina.

Computers in biology and medicine
|July 4, 2025
PubMed
概括

最大度调整通过提高不确定性估计来增强白质超强度 (WMH) 分段. 这有助于在不同的临床环境中识别细分错误,而不需要基本真相标签.

关键词:
域名转移 域名转移域名转移最大度调节的规范化医疗图像细分 医疗图像细分不确定性估计估计不确定性白质的超强度是白质的超强度.

更多相关视频

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.0K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K

相关实验视频

Last Updated: Sep 16, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.4K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.0K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K

科学领域:

  • 医学成像分析分析 医学成像分析
  • 深度学习用于神经成像.

背景情况:

  • 精确的白质超强度 (WMH) 分段对于诸如多发性硬化症等神经疾病至关重要.
  • 磁共振成像数据的域位移 (例如,不同的机器或参数) 挑战模型校准和不确定性估计.
  • 预测不确定性可以作为一个代理来识别部署后的错误,而没有基准真相标签.

研究的目的:

  • 调查域位移对WMH细分精度的影响.
  • 评估最大度规范化技术,以提高模型校准和不确定性估计在WMH细分.
  • 在临床部署中使用预测不确定性识别潜在的细分错误.

主要方法:

  • 使用U-Net架构进行WMH细分.
  • 应用和评估的最大规范化方案.
  • 在两个公共数据集上进行了测试:WMH细分挑战和3D-MR-MS.
  • 使用子系数,豪斯多夫距离,预期校准误差和基于的不确定性来评估性能.

主要成果:

  • 基于的不确定性估计有效地预测跨不同数据分布的细分错误.
  • 最大度规范化加强了不确定性和细分性能之间的联系.
  • 在域转移条件下,模型校准得到了改进.

结论:

  • 最大度规范化增强了WMH细分的不确定性估计,特别是在域转移下.
  • 这些方法使得可靠的标记不可靠的预测,没有基础真相注释.
  • 改进的模型校准支持在异质的临床环境中更安全地部署深度学习.