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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

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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.
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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Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...
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相关实验视频

Updated: Jul 3, 2025

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

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使用多重预处理和优化CNN模型的白细胞分类.

Oumaima Saidani1, Muhammad Umer2, Nazik Alturki1

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific reports
|February 12, 2024
PubMed
概括
此摘要是机器生成的。

这项研究通过使用先进的预处理和数据增强,从显微镜图像中增强了白细胞 (WBC) 的分类. 一种新的深度学习方法达到0.99准确度,超过了免疫细胞分析的现有方法.

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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Discrimination and Characterization of Heterocellular Populations Using Quantitative Imaging Techniques
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科学领域:

  • 血液学 血液学 血液学
  • 医疗成像医学成像
  • 计算生物学 计算生物学

背景情况:

  • 白细胞 (WBC) 对免疫反应至关重要,它们的异常标志着白血病等疾病.
  • 以前的WBC分类研究往往缺乏准确性,原因是特征集有限,并且专注于较少的细胞类型.

研究的目的:

  • 开发一种高精度和计算效率的方法,用微观图像对WBC类型进行分类.
  • 通过改善特征提取和模型性能来解决现有的WBC分类技术的局限性.

主要方法:

  • 采用广泛的预处理和数据增强技术来生成一个强大的功能集.
  • 利用传统的深度学习和转移学习模型来进行WBC图像分类.
  • 将拟议的方法与最先进的机器和深度学习模型进行比较.

主要成果:

  • 拟议的方法使用预处理的特征集与卷积神经网络,实现了0.99.9的分类准确性.
  • 与现有的最先进的方法相比,证明了卓越的性能和计算效率.
  • 成功解决了分类多种WBC类型的高精度挑战.

结论:

  • 这种新的方法显著提高了WBC分类的准确性和效率.
  • 这种方法为血液学中的自动诊断工具提供了有希望的进步.
  • 突出了医疗图像分析中先进特征工程的重要性.