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基于卷积神经网络和自我注意的多实例学习方法,用于早期癌症检测.

Junjiang Liu1, Shusen Zhou1, Mujun Zang1

  • 1School of Information and Electrical Engineering, Ludong University, Shandong, China.

Computer methods in biomechanics and biomedical engineering
|December 7, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了MICA,这是一种新的深度学习方法,用于使用T细胞受体测序 (TCR-seq) 早期发现癌症. MICA显著提高了肺癌和甲状腺癌的诊断准确度.

关键词:
早期癌症检测 早期癌症检测一个T细胞受体序列.可以解释的解释性.多个实例的学习学习多个实例的学习.自我注意力机制机制

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科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能

背景情况:

  • 早期癌症检测对于改善患者的治疗结果至关重要.
  • T细胞受体测序 (TCR-seq) 显示了癌症诊断的前景.
  • 现有的方法可能缺乏早期识别所需的精度.

研究的目的:

  • 开发一种先进的多重实例学习 (MIL) 方法,用于增强癌症检测.
  • 利用深度学习,特别是卷积神经网络 (CNN) 和自我注意力,用于分析TCR-seq数据.
  • 用基因组生物标志物提高早期癌症识别的准确性和效率.

主要方法:

  • 开发了MICA,一种集成CNN和自我注意的多实例学习方法.
  • 预处理的TCR-seq数据使用词向量进行特征提取.
  • 采用了增强的自我注意机制,以捕捉TCR-seq数据中的实例关系.
  • 使用交叉验证进行严格的绩效评估.

主要成果:

  • 在肺癌检测中,MICA实现了0.911的曲线下面面积 (AUC).
  • 在甲状腺癌检测中,MICA的AUC值为0.946.
  • 与现有方法相比,表现出显著的性能改善,肺癌和甲状腺癌的AUC分别增加了7.1%和2.1%.

结论:

  • MICA是一种高效的深度学习方法,用于通过TCR-seq.q.通过早期癌症检测.
  • 该方法在识别肺癌和甲状腺癌方面表现出卓越的性能.
  • MICA为推进精确瘤学和癌症诊断提供了一个有前途的工具.