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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

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...

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相关实验视频

Updated: Jun 19, 2026

Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone
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血细胞图像细分和分类:系统性审查

Muhammad Shahzad1, Farman Ali2, Syed Hamad Shirazi1

  • 1Department of Computer Science and Information Technology, Hazara University, Mansehra, Pakistan.

PeerJ. Computer science
|March 4, 2024
PubMed
概括
此摘要是机器生成的。

这项调查回顾了血液细胞分析的深度学习,强调了细分,分类和特征选择方法,用于诊断白血病和贫血等疾病. 调查结果显示,手动图像采集和形态特征的趋势,表明需要标准化数据集.

关键词:
分类 分类 分类 分类.形态特征 形态特征 形态特征红细胞是血液中的红细胞.分段化 分段化 分段化 分段化白细胞是白细胞的组成部分.

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科学领域:

  • 医学图像分析 医学图像分析
  • 计算病理学计算病理学
  • 血液学 血液学 血液学

背景情况:

  • 血液疾病 (白血病,贫血,淋巴瘤,血症) 涉及白细胞 (WBC) 和红细胞 (RBC) 的异常.
  • 准确的诊断依赖于血液学家的专业知识,对计算机辅助诊断 (CAD) 技术越来越感兴趣.
  • 现有的调查缺乏整体观点,只关注细分或分类等特定方面.

研究的目的:

  • 通过深度学习提供血液图像分析的全面和系统审查.
  • 专注于医疗图像处理和WBC和RBC形态表征的深度学习.
  • 涵盖细分,分类,特征选择,评估参数和数据集选择.

主要方法:

  • 在血液图像分析中对深度学习技术的系统文献综述.
  • 对细分,分类,特征选择,评估矩阵和数据集选择方法的分析.
  • 专注于白细胞和红细胞的形态特征.

主要成果:

  • 研究人员经常手动获取图像 (50%用于WBC细分,60%用于RBC细分).
  • 在WBC分类中,ALL-IDB数据集是常见的 (45%),而RBC分类经常使用手工获取的图像 (73%).
  • 形态特征是对分类的首选 (55%为白细胞,80%为红细胞).

结论:

  • CAD技术,特别是深度学习,可以提高血液疾病的诊断.
  • 不一致的数据集选择凸显了对标准化,高质量的数据集的需求.
  • 未来的研究应该探索和创新形态特征和标准化数据集.