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相关概念视频

Ultrasonography01:17

Ultrasonography

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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相关实验视频

Updated: Jul 26, 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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多用户多目标计算卸载用于医学图像诊断.

Qi Liu1,2, Zhao Tian3, Guohua Zhao4

  • 1School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China.

PeerJ. Computer science
|June 22, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种用于医学图像诊断的新型计算卸载策略,考虑了用户的风险和成本. 拟议的算法有效地管理多个用户的资源,增强诊断任务处理.

关键词:
计算卸载 计算卸载分布式优化 分布式优化准确的潜在游戏潜力.多个目标的多重目标.预期理论的前景理论.风险意识 风险意识

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Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis
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Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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相关实验视频

Last Updated: Jul 26, 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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Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis
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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

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科学领域:

  • 计算机科学 计算机科学
  • 医疗信息学 医疗信息学
  • 分布式系统 分布式系统

背景情况:

  • 计算卸载解决了医疗设备中有限的计算资源,通过利用边缘服务器来完成像图像诊断这样的任务.
  • 现有的卸载策略往往忽略了关键因素,如个人用户的风险意识和各种处理成本.
  • 有效的计算卸载对于及时准确的医学图像分析至关重要.

研究的目的:

  • 为医疗图像诊断提出一个多用户,多目标计算卸载策略.
  • 将用户的风险意识,延迟,能源消耗和支付纳入公用事业功能.
  • 开发一种低复杂度的算法,优化卸载决策,以提高诊断效率.

主要方法:

  • 设计了一个前景理论效用函数,集成延迟,能源,支付和风险意识.
  • 制定了卸载问题作为一个旨在最大限度地提高用户实用性的分布式优化问题.
  • 将问题转化为非合作游戏,使用精确的潜在游戏理论证明纳什平衡点.
  • 开发了一个基于最佳响应动态的低复杂度计算卸载算法.

主要成果:

  • 与基准和启发式方法相比,拟议的算法显示了更快的趋同.
  • 该算法实现了最小的1.14%的实用价值下降,即使用户数量越来越多.
  • 数字实验验证了算法的性能和各种参数对实用性的影响.

结论:

  • 开发的计算卸载策略有效地平衡了医学图像诊断中的多个目标.
  • 该算法为资源有限的医疗环境提供了高效和可扩展的解决方案.
  • 这种方法通过考虑用户特定的因素,提高了用于医学图像诊断的计算卸载的实用性.