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

Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

13.1K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
13.1K

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

A Lightweight ScaleDense-Transformer Framework with Auxiliary Quantum-Inspired Bottleneck Module for Whole-Lifespan Brain Age Prediction.

Brain sciences·2026
Same author

Pharmacotherapy for Children and Adolescents With Overweight or Obesity: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials.

Diabetes, obesity & metabolism·2026
Same author

Sex differences in resting-state hypothalamic connectivity and its relationship with negative emotion and behavior measures in young adults.

Brain structure & function·2026
Same author

Prosodic Resolution of Quantifier Scope Ambiguity in Child Mandarin.

Journal of psycholinguistic research·2026
Same author

Personalized Simulation Modeling of Overlapping Microwave Ablation for Large Tumors.

Bioengineering (Basel, Switzerland)·2026
Same author

A large-scale 12-lead electrocardiogram dataset for acute coronary syndrome prediction containing 19,955 ECGs.

Scientific data·2026

相关实验视频

Updated: Jun 7, 2025

A Novel Approach to Overcome Movement Artifact When Using a Laser Speckle Contrast Imaging System for Alternating Speeds of Blood Microcirculation
07:20

A Novel Approach to Overcome Movement Artifact When Using a Laser Speckle Contrast Imaging System for Alternating Speeds of Blood Microcirculation

Published on: August 30, 2017

8.4K

量子机器学习增强了激光光斑分析,用于精确的速度预测.

YiXiong Chen1, WeiLu Han2, GuangYu Bin2

  • 1Beijing Science and Technology Project Manager Management Corporation Ltd, Beijing, 100083, China.

Scientific reports
|November 12, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种混合量子-经典3D CNN用于激光光斑对比成像 (LSCI),改进了血液流量的量化. 这种新的方法提高了预测准确性和学习稳定性,使得血液流量分析更可靠.

关键词:
血液流动成像 血液流动成像混合动力模型 混合动力模型激光光斑对比成像 激光光斑对比成像量子机器学习就是量子机器学习.变量量子算法变量量子算法速度预测的预测速度.

更多相关视频

Quantitative Locomotion Study of Freely Swimming Micro-organisms Using Laser Diffraction
10:03

Quantitative Locomotion Study of Freely Swimming Micro-organisms Using Laser Diffraction

Published on: October 25, 2012

11.5K
Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers
04:36

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

Published on: September 1, 2023

3.2K

相关实验视频

Last Updated: Jun 7, 2025

A Novel Approach to Overcome Movement Artifact When Using a Laser Speckle Contrast Imaging System for Alternating Speeds of Blood Microcirculation
07:20

A Novel Approach to Overcome Movement Artifact When Using a Laser Speckle Contrast Imaging System for Alternating Speeds of Blood Microcirculation

Published on: August 30, 2017

8.4K
Quantitative Locomotion Study of Freely Swimming Micro-organisms Using Laser Diffraction
10:03

Quantitative Locomotion Study of Freely Swimming Micro-organisms Using Laser Diffraction

Published on: October 25, 2012

11.5K
Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers
04:36

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers

Published on: September 1, 2023

3.2K

科学领域:

  • 生物医学光学 生物医学光学
  • 量子计算是一种量子计算.
  • 机器学习 机器学习

背景情况:

  • 激光斑点对比成像 (LSCI) 对于评估血流输液至关重要,但在定量分析中面临局限性.
  • 三维卷积神经网络 (3D CNN) 通过提取时空特征来提高LSCI的定量性能.
  • 在3D CNN中过度下采样可能导致关键信息丢失,阻碍精确的血流量化.

研究的目的:

  • 开发一个混合量子-经典3D CNN框架,以提高LSCI的定量性能.
  • 通过整合变量量子电路 (VQC) 来解决传统3D CNN中的信息丢失问题.
  • 在LSCI中提高血流速度预测的准确性和稳定性.

主要方法:

  • 提出了一个混合量子-经典3D CNN框架,利用变量量子算法 (VQA).
  • 用变量量子电路 (VQC) 取代3D全球聚合层,以增强功能集成.
  • 通过对实验LSCI光斑数据的交叉验证和对未见测试集的评估来验证框架.

主要成果:

  • 与经典的3D CNN相比,混合模型显示出更高的预测准确性和学习稳定性.
  • 在一个看不见的测试组中,平均平方误差 (MSE) 提高了14.8%,平均绝对百分比误差 (MAPE) 提高了26.1%.
  • 定性分析证实了在预测低血流速度和高血流速度方面取得的实质性改进.

结论:

  • 混合量子-经典3D CNN框架显著提高了LSCI在血液流量分析方面的定量能力.
  • VQC有效地保存时空信息,导致更强大的学习和概括.
  • 这种方法为推进定量光学成像技术提供了一个有希望的方向.