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相关概念视频

Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

25
Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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External Anatomy of the Kidney01:21

External Anatomy of the Kidney

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The kidneys are a pair of bean-shaped organs in the human body that play a critical role in maintaining overall health. They filter out waste products from the blood, regulate blood pressure, maintain electrolyte balance, and stimulate the production of red blood cells.
The kidneys are located in the retroperitoneal space on either side of the vertebral column, protected posteriorly by the 11th and 12th ribs. The right kidney sits slightly lower than the left owing to the presence of the liver...
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相关实验视频

Updated: Jul 16, 2025

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
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没有注释的脏组织病理图像分析与深度集体学习.

Jia Chun Koo1, Qi Ke2, Yan Chai Hum1

  • 1Lee Kong Chian Faculty of Engineering and Science, University Tunku Abdul Rahman, Kajang, Malaysia.

Quantitative imaging in medicine and surgery
|September 15, 2023
PubMed
概括

这项研究引入了一个深度学习模型,用于在组织病理学图像中自动检测癌. 新型组合方法显著提高了诊断准确度,有助于早期检测和患者的生存.

关键词:
深度学习是一种深度学习.组合学习组合学习组织病理学图像 组织病理学图像癌 癌 癌 癌 癌转移学习转移学习

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Whole-Kidney Three-Dimensional Staining with CUBIC

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相关实验视频

Last Updated: Jul 16, 2025

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Application of Laser Microdissection to Uncover Regional Transcriptomics in Human Kidney Tissue
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科学领域:

  • 在瘤学瘤学.
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 癌对全球健康构成重大威胁,早期发现对于改善患者的治疗结果至关重要.
  • 目前对脏组织的手动分析是耗时的,可变的,可能会错过早期癌症标志物.

研究的目的:

  • 开发一个使用深度学习进行细胞病理图像分析的自动化系统.
  • 提高癌的早期检测,减少诊断错误.

主要方法:

  • 开发了使用VGG,ResNet,DenseNet,MobileNet和EfficientNet架构的深卷积神经网络 (CNN) 的异质集合模型.
  • 图像被细分为补丁,使用预训练的CNN被分类为正常或瘤,并通过合体学习组合在一起进行最终分类.
  • 精选的高性能CNN用于组合学习,以利用多种模式的优势.

主要成果:

  • 性能最好的模型,五个CNN加权平均组合,实现了99%的准确性,98%的特异性,99%的F1得分和98%的ROC AUC.
  • 与现有的最先进的方法相比,在病原体图像分析中表现出卓越的性能.

结论:

  • 开发的整体模型显示出高强度和可靠性,用于协助病理学家进行脏组织分析.
  • 这种人工智能驱动的方法可以提高早期癌检测的效率,最大限度地减少误诊,并最终提高患者的生存率.