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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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相关实验视频

Updated: Jun 29, 2025

Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia
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Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia

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基于ACGANAN的小样本的AML白细胞分类方法.

Chenxuan Zhang1, Junlin Zhu2

  • 1School of Artificial Intelligence, 232838 Chongqing University of Technology , Chongqing, PR.China.

Biomedizinische Technik. Biomedical engineering
|March 28, 2024
PubMed
概括
此摘要是机器生成的。

辅助分类生成对抗网络 (ACGAN) 有效地分类小白细胞样本. 这种深度学习方法显示出高精度,超过了白血病诊断的其他方法.

关键词:
在ACGANAN中,我们可以看到ACGAN数据增强数据增强图像的分类图像的分类.植物小规模数据集

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Processing of Bronchoalveolar Lavage Fluid and Matched Blood for Alveolar Macrophage and CD4+ T-cell Immunophenotyping and HIV Reservoir Assessment
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Isolation and In Vitro Culture of Murine and Human Alveolar Macrophages
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Isolation and In Vitro Culture of Murine and Human Alveolar Macrophages

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Processing of Bronchoalveolar Lavage Fluid and Matched Blood for Alveolar Macrophage and CD4+ T-cell Immunophenotyping and HIV Reservoir Assessment
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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
  • 人工智能的人工智能

背景情况:

  • 急性髓性白血病 (AML) 的诊断依赖于对外围血液涂抹的显微镜分析.
  • 准确识别和计数白细胞对于诊断血性恶性瘤如AML至关重要.
  • 血液细胞识别的深度学习 (DL) 方法需要大量的数据集,对小样本大小构成挑战.

研究的目的:

  • 用有限的数据评估辅助分类生成对抗网络 (ACGAN) 对白细胞分类的有效性.
  • 在小样本场景中,将ACGAN的性能与传统分类器和当前最先进的方法进行比较.

主要方法:

  • 在ACGAN的培训中,ACGAN使用了TCIA的白细胞分类数据集.
  • 使用准确度,精度,回忆和F1分数来评估性能.
  • 结果与两个经典分类器和先进的深度学习技术进行了比较.

主要成果:

  • 在验证组中,ACGAN 实现了 97.1% 的准确性.
  • 对ACGAN的精度,回忆和F1分数分别为97.5%,97.3%和97.4%.
  • 与其他先进方法相比,ACGAN表现出优越或具有竞争力的分类准确性.

结论:

  • ACGAN是一种可行且具有竞争力的深度学习方法,用于分类小白细胞样本数据集.
  • 该方法显示了提高AML查和诊断的准确性和效率的巨大潜力.
  • ACGAN解决了在将深度学习应用于医疗图像分析时经常遇到的数据限制.