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使用转移学习方法进行免疫固定电泳图像解释

Berrin Oztas1, Irfan Kosesoy2

  • 1Department of Biochemistry, School of Medicine, Kocaeli University, Kocaeli, Turkey.

Clinica chimica acta; international journal of clinical chemistry
|November 21, 2025
PubMed

Insights

深度学习模型准确地解释免疫固定电泳 (IFE) 图像,减少诊断主观性. 这些人工智能工具对实验室集成有希望,改善了血细胞疾病的分析.

科学领域:

  • 医学诊断 医学诊断 医学诊断
  • 人工智能的人工智能
  • 计算病理学计算病理学

背景情况:

  • 免疫固定电泳 (IFE) 是诊断血细胞疾病的标准,但复杂和主观.
  • 观察者之间的变化和时间限制挑战了IFE目前的解释.
  • 开发自动化解释方法对于高效准确的诊断至关重要.

研究的目的:

  • 开发和评估用于自动化IFE图像解释的深度学习模型.
  • 为了减少主观性和提高单克隆蛋白质识别的速度.
  • 评估IFE分析的不同深度学习架构的性能.

主要方法:

  • 使用了5226张IFE图像的数据集,分为培训 (80%) 和测试 (20%) 集.
  • 开发了两个深度学习方法:一个双阶段 (二进制,然后是子类) 和一个单阶段的多类模型.
  • 使用YOLOv11架构的转移学习,使用包括准确性,精度,回忆和F1分数在内的性能指标.

主要成果:

  • 两阶段模型在二进制分类方面达到94.76%的准确性,在子类识别方面达到91.28%的准确性.
  • 单阶段多类模型的整体准确性略高,为92.12%.
  • 两种模型都显示出对主导模式的高准确性,但在代表性不足或视觉上相似的阶级方面遇到了困难.

结论:

  • 深度学习模型可以准确地分类IFE图像,证明了实验室工作流集成的潜力.
  • 两个阶段的方法提供了更好的解释性,而多类模型提供了可扩展性和效率.
  • 未来的工作应该集中在扩大罕见模式的数据集和改善低频类别的分类.
抽象的

No abstract available in PubMed .

关键词:
人工智能的人工智能是人工智能.临床决策支持 临床决策支持深度学习是一种深度学习.免疫固定电泳是一种电泳.单克隆性胃病变是一种单克隆性胃病变.转移学习转移学习

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