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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

4.8K
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.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
4.8K
Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
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Classification of Connective Tissues01:30

Classification of Connective Tissues

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The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
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Aggregates Classification01:29

Aggregates Classification

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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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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相关实验视频

Updated: Jan 6, 2026

A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections
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宫整体幻灯片图像数据集用于多类分类.

Mahnaz Mohammadi1, Christina Fell1, David Morrison1

  • 1School of Medicine, University of St Andrews, North Haugh, St Andrews, KY16 9TF, United Kingdom.

GigaScience
|November 29, 2025
PubMed
概括

这项研究为机器学习引入了大量注释性宫活检图像的数据集. 这种资源使幻灯片的自动分析成为可能,改善了宫癌诊断和患者选.

关键词:
宫癌:子宫癌是一种癌症.宫的子宫可以深度学习是一种深度学习.数字图像数据库 数字图像数据库医疗保健数据集 医疗保健数据集组织病理学 组织病理学机器学习是机器学习.整个幻灯片成像成像技术

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Digital Analysis of Immunostaining of ZW10 Interacting Protein in Human Lung Tissues
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Last Updated: Jan 6, 2026

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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
  • 机器学习在医疗保健中的应用

背景情况:

  • 宫癌的诊断依赖于细胞学和活检样本的手动审查.
  • 活检幻灯片的组织学检查给病理学家带来了相当大的工作负载.
  • 缺乏大型的注释数据集阻碍了用于诊断辅助的AI工具的开发.

研究的目的:

  • 创建和共享一个全面的数据集注释的宫活检全幻灯片图像.
  • 促进用于宫癌诊断的机器学习算法的开发和验证.
  • 解决计算病理学中有限的注释数据的障碍.

主要方法:

  • 编制了一个由2539个从宫活检样本的全幻灯片图像组成的数据集.
  • 图像由多位病理学家手动注释,对诊断和特征达成共识.
  • 提供了Jason格式的注释,对应于iSyntax全幻灯片图像.

主要成果:

  • 数据集包括在子幻灯片层面上的详细注释.
  • 每个图像都被赋予一个共识诊断和子类别标签.
  • 这一数据集因其规模和公众可访问的注释水平而独一无二.

结论:

  • 该数据集使得开发一个准确的诊断预测模型成为可能.
  • 活检样本的自动分类可以加快显著病理的识别.
  • 本资源支持计算机视觉研究,用于人类组织诊断.