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相关概念视频

Structure and Function of Leukocytes01:21

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An adult in good health typically has between 4,500 and 11,000 leukocytes, or white blood cells, per microliter of blood, which constitutes about 1% of the total blood volume. Unlike red blood cells, white blood cells contain a nucleus and other cellular organelles but do not have hemoglobin. Most white blood cells reside in connective tissues, particularly in lymphatic organs such as the lymph nodes, with only a small fraction present in circulating blood.
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Classification of Leukocytes01:30

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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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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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预先训练有素的深度学习模型,用于高效的白细胞分类.

P Jeneessha1, Vinoth Kumar Balasubramanian2

  • 1Department of Information Technology, PSG College of Technology, Coimbatore, 641004, India. jen.it@psgtech.ac.in.

Scientific reports
|May 4, 2025
PubMed
概括

将域名知识与图像数据集成显著提高深度学习模型中的白细胞 (WBC) 分类准确性. 这种方法增强了计算机辅助诊断 (CAD) 系统,提高了医疗分析的效率和可靠性.

关键词:
分类 分类 分类 分类.域名知识 域名知识特性向量是一个特征向量.预先训练有素的模型.白细胞是白细胞的组成部分.

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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 准确的白细胞 (WBC) 分类对于患者的健康评估和治疗验证至关重要.
  • 现有的计算机辅助诊断 (CAD) 系统面临医疗数据集数据不足的挑战,限制了深度学习模型的性能.
  • 数据增强和规范化提高了数据的数量而不是质量,强调了需要加强数据利用的必要性.

研究的目的:

  • 通过将域名知识与图像数据集成,提高预训练深度学习模型的分类性能.
  • 在Inception V3,DenseNet 121,ResNet 50,MobileNet V2和VGG 16.等模型上评估领域知识输入的有效性.
  • 分析BCCD和LISC数据集的性能改进情况.

主要方法:

  • 利用域名知识和图像数据融合来增强预先训练的深度学习模型.
  • 在BCCD和LISC数据集上应用了增强模型来对WBC进行分类.
  • 对比了具有和没有注入领域知识的模型的性能.

主要成果:

  • 在BCCD数据集上,在纳入领域知识后,所有测试模型 (Inception V3,DenseNet 121,ResNet 50,MobileNet V2,VGG 16) 的平均准确性都增加了.
  • 在LISC数据集上也观察到显著的准确性改进,模型显示后域知识输入后性能增强.
  • 该研究通过将领域专业知识与医疗图像数据集的深度学习相结合,在WBC分类准确度方面取得了实质性进展.

结论:

  • 将域名知识与图像数据集成是改进基于深度学习的WBC分类的高效策略.
  • 这种方法为医疗数据集中的数据不足问题提供了可行的解决方案,提高了CAD系统的准确性.
  • 这些发现支持使用领域知识注入的深度学习模型,以实现更高效,更可靠的医学图像分析.