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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

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...
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

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,...
Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...

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相关实验视频

Updated: Jun 18, 2026

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
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Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

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在整个幻灯片成像中探索多实例学习:当前和未来的观点.

Jikai Yu1, Hongda Chen1, Lianxin Hu1

  • 1School of Information Engineering, Huzhou University, Huzhou, ZheJiang 313000, China.

Pathology, research and practice
|May 14, 2025
PubMed
概括
此摘要是机器生成的。

整个幻灯片图像 (WSIs) 对病理学至关重要,但对GPU来说太大了. 多实例学习 (MIL) 能够有效地分析这些大型 WSIs 以实现自动诊断.

关键词:
深度学习是一种深度学习.在MIL应用程序中,MIL应用程序.多个实例的学习 (MIL)整个幻灯片图像的图像.

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相关实验视频

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科学领域:

  • 数字病理学数字病理学
  • 计算病理学计算病理学
  • 机器学习在医学中的应用

背景情况:

  • 整个幻灯片图像 (WSIs) 对于诊断病理学至关重要,但由于它们的大小,会带来计算挑战.
  • 在GPU上直接处理千兆字节规模的WSIs很困难,这阻碍了自动选和诊断算法开发.

研究的目的:

  • 系统地审查研究进展和多实例学习 (MIL) 的应用,以分析世界互联网.
  • 概述MIL在WSI处理和分析癌症检测和亚型分类的主流技术方面的优势和改进.

主要方法:

  • 系统的文献综述来自Web of Science,IEEE Xplore和PubMed的90多篇文章.
  • 应用到WSI分析的主流MIL技术的核心特征和性能分析.
  • 专注于增强WSI处理MIL能力的方法.

主要成果:

  • 多实例学习 (MIL) 为WSI分析提供了一个有效的计算框架.
  • 诸如数据预处理,多尺度特征融合,实例选择和变压器模型等技术显著提高了WSIs上的MIL性能.
  • 在诸如癌症检测和WSIs的亚型分类等任务中,MIL显示出前景.

结论:

  • MIL是一种强大的方法,可以克服WSI分析中的计算限制.
  • MIL方法的进步对于开发自动化病理诊断工具至关重要.
  • 未来的研究应该专注于进一步增强MIL技术,以便在病理学中更广泛地采用.