跨模式对齐和对比学习,以提高癌症存活率预测
Tengfei Li1, Xuezhong Zhou1, Jingyan Xue1
1School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China.
Computer methods and programs in biomedicine
|February 17, 2025
概括
这项研究介绍了CPathomic,一种使用病理图像和基因组学预测癌症存活率的新方法. CPathomic有效地整合了这些数据类型,大大提高了预测准确度.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 计算病理学计算病理学
背景情况:
- 整合多模式数据 (病理图像,基因组学) 对癌症研究至关重要.
- 目前的预测方法通常依赖于单个数据域,限制了预测准确度.
- 现有的多式联运方法难以捕捉复杂的多式联运关系.
研究的目的:
- 开发一种用于基于多式联运数据的生存预测的新方法.
- 解决单域预后模型的局限性.
- 通过有效地融合病理学和基因组数据来提高患者结果预测.
主要方法:
- 介绍了多模式生存预测的CPathomic方法.
- 利用全片病理图像来指导局部病理特征的提取.
- 采用跨模式的代表性对比学习来缓解跨模式差异.
- 实施了跨模式和封闭式注意力模块,以便在模式之间进行交互式学习.
主要成果:
- CPathomic有效地弥合了多式联运数据中的模式差距.
- 该框架的表现始终优于其他多式联运生存预测方法.
- 在5个公共TCGA数据集上进行了实验.
结论:
- CPathomic 展示了对比式学习和跨模式注意力的潜力,以实现多模式数据融合.
- 拟议的模型增强了患者生存预测的性能.
- 这种方法提供了对癌症异质性的更全面的理解.
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