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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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相关实验视频

Updated: Apr 29, 2026

Rearing Ixodes scapularis, the Black-legged Tick: Feeding Immature Stages on Mice
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使用显微镜的高分辨率CMOS传感器捕获的图像上的Ixodes ricinus的深度学习方法

Aleksandra Marzec1,2, Anna Filipowska2, Oliwia Humeniuk2

  • 1Foundation of Cardiac Surgery Development, Institute of Heart Prostheses, 345a Wolności, 41-800 Zabrze, Poland.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个深度学习模型来分类欧洲的发育阶段. 这种人工智能工具有助于通过准确识别幼虫,和成年人来了解传播的疾病.

关键词:
其他国家可解释的AI (XAI)一个Grad-CAM鱼类深度学习图像分类的发展阶段

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A Capsule-Based Model for Immature Hard Tick Stages Infestation on Laboratory Mice
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科学领域:

  • 昆虫学
  • 计算机科学
  • 公共卫生

背景情况:

  • 它是欧洲最常见的虫,也是莱姆病菌和TBEV等病原体的重要载体.
  • 不同的发育阶段 (幼虫,,成年雌性,成年雄性) 在疾病传播中起着不同的作用.
  • 由于它们的发病率和小体积,在流行病学上尤为重要,因此精确的鉴定至关重要.

研究的目的:

  • 开发和验证深度学习模型来分类的发育阶段.
  • 解决自动识别欧洲物种阶段的需求,与现有的以北美为重点的解决方案区别.

主要方法:

  • 一个卷积神经网络 (CNN) 模型被开发和训练在微观图像的.
  • 图像数据来自波兰上西里西亚地区.
  • 使用可解释人工智能 (XAI) 技术Grad-CAM可视化模型决策过程.

主要成果:

  • 通过CNN模型对的发育阶段进行了有效分类.
  • 这项研究是首次将CNN用于识别欧洲动物的发育阶段.
  • 格拉德-CAM分析提供了对模型分类精度至关重要的图像特征的见解.

结论:

  • 开发的深度学习方法提供了一种新且准确的方法来识别的发育阶段.
  • 这项技术在昆虫学研究,公共卫生监测和传播疾病管理方面具有重要的潜在应用.
  • 该模型为区分病阶段提供了有价值的工具,改善了流行病学评估和控制策略.