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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

123
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
123

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

<i>Bacillus pumilus</i> AD14: A Saline-Alkali-Tolerant Plant Growth-Promoting Bacterium for Enhancing Soybean Tolerance and Ameliorating Saline-Alkali Soil.

Microorganisms·2026
Same author

Ultrasensitive SERS platform for cisplatin detection using self-assembled gold nanooctahedra-silver nanocube core-shell nanoparticles.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Natural killer cell-mediated immunosurveillance modulates liver cancer evolution through cancer stemness enhancement and lipid metabolism reprogramming.

Nature communications·2026
Same author

Comparative long-term outcomes of home-based versus institutional care in Alzheimer's disease.

Journal of Alzheimer's disease : JAD·2026
Same author

Teaching artificial intelligence through drug-drug interaction clustering analysis: Integrating project-based learning and large language models.

PLoS computational biology·2026
Same author

Case Report: Sequential FcRn blockade and B-cell depletion for treatment-refractory relapsing autoimmune encephalitis: a three-patient case series.

Frontiers in immunology·2026
Same journal

Corrigendum to "Integrating experimental biology, computational methods, and artificial Intelligence in anticancer drug discovery: Bridging the translational Gap" [Comput. Biol. Med. 213 (2026) 111832].

Computers in biology and medicine·2026
Same journal

Organ dose optimization for a point-of-care forearm X-ray photon-counting CT.

Computers in biology and medicine·2026
Same journal

Physics-guided transformation of breathomic feature spaces into disease-specific representations for respiratory disease classification.

Computers in biology and medicine·2026
Same journal

An AI-driven deep learning pipeline for taxonomic classification and biodiversity assessment of deep-sea environmental DNA.

Computers in biology and medicine·2026
Same journal

Rapid personalisation of cardiovascular models using invasively measured right ventricular pressure.

Computers in biology and medicine·2026
Same journal

Biologically inspired mechanisms for enhancing robustness in EEG signal modeling: Challenges, opportunities, and perspectives.

Computers in biology and medicine·2026
查看所有相关文章

相关实验视频

Updated: Jun 26, 2025

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

2.7K

基于多策略学习的粒子群优化算法用于COVID-19值细分的粒子群优化算法.

Donglin Zhu1, Jiaying Shen1, Yangyang Zheng1

  • 1School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.

Computers in biology and medicine
|May 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了基于内部的多策略粒子群集优化 (IPSOsono) 以改善COVID-19医疗图像细分. IPSOsono提高了门准确度,克服了传统方法的局限性,以获得更好的诊断洞察力.

关键词:
在 COVID-19 疫情中,全球优化全球优化粒子群集优化优化 粒子群集优化门细分是指门的细分.

更多相关视频

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

10.1K
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.1K

相关实验视频

Last Updated: Jun 26, 2025

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

2.7K
Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

10.1K
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.1K

科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 计算优化计算优化

背景情况:

  • 医学图像分析对于COVID-19研究至关重要,图像细分是关键的初步步骤.
  • 图像分割的传统值方法在最佳值选择和效率方面面临挑战.
  • 现有的算法在复杂的优化任务中扎着停滞和局部最佳值.

研究的目的:

  • 引入一种增强的优化算法,即基于内部的多策略粒子群优化 (IPSOsono),用于医疗图像细分.
  • 为了提高值图像细分的准确性和效率,专门用于COVID-19医疗图像.
  • 解决医疗图像分析中的传统值和现有优化技术的局限性.

主要方法:

  • 开发了IPSOsono,通过将一个新的动态振荡重量从单个目标数值优化 (PSOsono) 的粒子集群优化变体中结合起来.
  • 在粒子群中执行历史最佳位置的随机更新,以减轻停滞和局部最佳.
  • 引入了内部选择学习机制,在位置更新期间动态改进全球最佳解决方案.

主要成果:

  • 与最近的算法相比,PSOscono在2013年CEC基准测试中表现出卓越的优化能力.
  • 与其他算法相比,IPSOsono在COVID-19医疗图像的最小交叉值上实现了更突出的细分能力.
  • 该算法在6张CT图像中显示出良好的概括性,验证了其实际适用性.

结论:

  • IPSOsono是一个有效和可行的优化算法,用于增强值图像分割.
  • 拟议的方法比现有的COVID-19医疗图像分析算法提供了显著的改进.
  • 在现实世界医学成像场景中,PSOscono的强大性能证实了其实用的实用性.