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相关概念视频

Two-Dimensional Microscopy in Microbiology01:29

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Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field 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.
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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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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Updated: Sep 13, 2025

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System
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奥肯:一个监督的进化优化尺寸缩小框架,用于整张幻灯片图像分类.

Soroush Oskouei1,2, André Pedersen3, Marit Valla4,5

  • 1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim, Norway.

Bioengineering (Basel, Switzerland)
|July 29, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了人工智能框架,用于使用数字病理学的肺癌亚型分类. 这种新的方法提高了分析整个幻灯片图像 (WSIs) 的准确性和效率,优于内部数据集上的现有模型.

关键词:
深度学习是一种深度学习.数字病理学数字病理学减少维度,减少维度.进化算法是一种进化算法.肺癌是一种肺癌.

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科学领域:

  • 数字病理学和计算瘤学
  • 医疗图像分析中的人工智能

背景情况:

  • 准确的肺癌亚型分类对于临床治疗至关重要.
  • 组织病理学图像分析面临挑战,通常需要免疫组织化学.
  • 数字病理学和人工智能为组织幻灯片分析提供了自动化解决方案.

研究的目的:

  • 为肺癌整体幻灯片图像 (WSI) 分类开发人工智能框架.
  • 用一种进化算法来对可调节的潜空间进行特征编码.
  • 提高WSI分类和细分的计算效率.

主要方法:

  • 开发一个具有可优化的内核的WSI分类框架.
  • 来自WSI补丁的特征编码使用进化算法进入潜在空间.
  • 与内部和外部肺癌WSI数据集的最新Vim4Path模型进行比较.

主要成果:

  • 拟议的框架在X2.5和X10放大度的内部数据集上,在准确度和F1得分方面超过了Vim-S16.
  • 最高的精度 (0.833) 和F1得分 (0.721) 在内部测试组上的×2.5放大时实现.
  • 在外部测试组中,Vim-S16在×10具有最高精度 (0.732),而OKEN-DenseNet121在×2.5具有最佳F1评分 (0.772).

结论:

  • 开发的AI框架显示了准确和高效的肺癌WSI分类的希望.
  • 基于进化算法的特征编码与现有模型相比,提供了具有竞争力的方法.
  • 未来的工作应该专注于动态调整进化算法的输出维度.