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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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Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
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一个基于注意力的深度学习,用于急性淋巴细胞白血病分类.

Malathy Jawahar1, L Jani Anbarasi2, Sathiya Narayanan3

  • 1Leather Process Technology Division, CSIR-Central Leather Research Institute, Chennai, India.

Scientific reports
|July 29, 2024
PubMed
概括
此摘要是机器生成的。

一个新的深度扩展残留卷积神经网络 (DDRNet) 准确地分类血液细胞,用于早期检测急性淋巴细胞白血病 (ALL). 这种人工智能模型实现了高精度,帮助血液学家进行诊断并减少工作量.

关键词:
注意层是一个注意层.计算机辅助诊断是一种计算机辅助的诊断.卷积神经网络是一种卷积神经网络.深度学习模型深度学习模型在白血病中,白血病.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 血液学 血液学 血液学

背景情况:

  • 急性淋巴细胞白血病 (ALL) 是一种骨髓恶性瘤,其特征是过度生产不成熟细胞.
  • 美国每年所有诊断接近6500例,占儿童癌症的很大一部分.
  • 计算机辅助诊断 (CAD) 系统对于血液学家来说越来越重要,以管理数据并提高诊断准确性.

研究的目的:

  • 引入一种新的深度扩展残留卷积神经网络 (DDRNet),用于自动化血细胞分类.
  • 为了提高特征提取和分类准确性,用于早期检测急性淋巴细胞白血病 (ALL).
  • 为应对CAD系统中的挑战,如消失梯度和特征歧视.

主要方法:

  • 该研究开发了一个DDRNet模型,其中包括深度残留扩展块 (DRDB),全球和本地特征增强块 (GLFEB),道和空间注意力块 (CSAB).
  • 该模型利用Tanh和sigmoid激活函数来实现非线性和特征度.
  • 一个Kaggle数据集包括16,249张血液细胞图像,分为四个类别,用于训练 (80%) 和测试 (20%).

主要成果:

  • DDRNet模型实现了99.86%的高训练精度和91.98%的测试精度.
  • 该模型显示了0.96的显著F1得分,表明了强大的分类性能.
  • 集成DRDB,GLFEB和CSAB块增强了具有最小计算复杂性的特征歧视.

结论:

  • 与现有方法相比,DDRNet模型在多类血细胞分类中提供了优越的性能.
  • 拟议的架构有效地解决了用于ALL检测的特征提取和分类方面的挑战.
  • 作为血液病学家的先进CAD工具,DDRNet显示出有前途,提高了诊断效率和准确性.