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

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

726
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...
726
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

554
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...
554
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.1K
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...
4.1K
Uncertainty: Overview00:59

Uncertainty: Overview

597
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.
597
Deconvolution01:20

Deconvolution

188
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
188
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

73.9K
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. 
73.9K

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

Near-Infrared Spectroscopy Combined With Skin Impedance for Detection of Skin Cancer in Primary Care.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)·2026
Same author

Relaxivity of Gadobutrol and Gadoteric Acid in Cerebrospinal Fluid at 3T.

Magnetic resonance in medicine·2026
Same author

Enhancing computation speed and accuracy in deep image prior-based parameter mapping.

Magnetic resonance in medicine·2025
Same author

Bayesian non-linear regression with spatial priors for noise reduction and error estimation in quantitative MRI with an application in T1 estimation.

Physics in medicine and biology·2020
Same author

A constrained singular value decomposition method that integrates sparsity and orthogonality.

PloS one·2019
Same author

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

Human brain mapping·2018

相关实验视频

Updated: Jul 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

570

在参数映射中用近似贝叶斯深度图像先验的Denoising和不确定性估计.

Max Hellström1, Tommy Löfstedt1,2, Anders Garpebring1

  • 1Department of Radiation Sciences, Umeå University, Umeå, Sweden.

Magnetic resonance in medicine
|August 15, 2023
PubMed
概括

深度图像先验 (DIP) 成功地消除了参数映射,减少了医疗成像中的噪音和不确定性. 这种方法具有适应性,不需要训练数据,尽管计算时间较长,但提供了强大的方法.

关键词:
深度图像之前的图像.拒绝的意思是拒绝.参数映射是指参数的映射.定量的MRI是指MRI的数量.不确定性估计估计的不确定性

更多相关视频

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.6K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

相关实验视频

Last Updated: Jul 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

570
Surface Mapping of Earth-like Exoplanets using Single Point Light Curves
06:48

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

3.6K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

科学领域:

  • 医疗成像医学成像
  • 计算神经科学是一种神经科学.
  • 机器学习 机器学习

背景情况:

  • 医学成像中的参数映射通常会受到噪音和高不确定性的影响.
  • 传统的方法很难有效地否定这些参数图.

研究的目的:

  • 通过使用深度图像先验 (DIP) 将参数映射作为否定任务来解决杂的参数地图和高不确定性.
  • 为了将DIP的无色化能力扩展到组织参数地图生成.

主要方法:

  • 通过将图像生成网络的输出作为组织参数图的参数化来利用深度图像先验 (DIP).
  • 采用未经训练的卷积神经网络 (CNN) 进行隐式拒绝,过低级图像特征.
  • 综合不确定性估计使用蒙特卡洛 (MC) 脱落用于voxel-wise不确定性量化.
  • 开发了一个模块化方法,允许适应各种应用程序,如T1映射,T2映射和明显扩散系数映射.

主要成果:

  • 在多个参数映射应用程序中证明了DIP的成功适应.
  • 与传统技术相比,实现了显著的噪声降低和参数图的不确定性降低.
  • 识别了延长的计算时间和潜在的偏差引入从先前的denoising作为限制.

结论:

  • 深度图像先验 (DIP) 有效地拒绝参数映射,并且可以在各种场景中使用,最小调整.
  • 由于没有培训数据要求,实现的方便性是一个关键优势.
  • 虽然在计算上密集,但MC中断衍生的不确定性信息增强了稳定性,并在校准时提供了有价值的见解.