MBFusion:用于癌症诊断和预后的多模式平衡融合和多任务学习
Ziye Zhang1, Wendong Yin1, Shijin Wang1
1Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis, School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641, Guangdong, China.
Computers in biology and medicine
|August 24, 2024
概括
MBFusion平衡病理图像和分子奥米克数据,以改善癌症诊断和预后预测. 这种多模式方法增强了特征表示,导致癌症亚型分类和生存分析的显著性能增长.
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:
- 病理图像和分子奥米克数据为癌症诊断和预后提供了互补的见解.
- 整合这些异质数据模式是具有挑战性的,因为不同的特征表示强度.
- 现有的多式联通融合方法往往无法达到最佳的结果,因为 modal 贡献不平衡.
研究的目的:
- 提出MBFusion,一个新的多式联通平衡融合框架用于癌症诊断和预后预测.
- 为加强癌症研究有效地整合病理图像和分子奥米克数据.
- 提高癌症亚型分类和生存分析的准确性.
主要方法:
- MBFusion 采用专门的图形卷积网络来进行分子奥米特征提取.
- 带有注意力和集群的ResNet用于病态图像特征提取,保留关键的深度特征.
- 交叉注意力变压器融合了两种模式的特征,随后进行了多任务学习以进行分类和生存分析.
主要成果:
- 在两个公共癌症数据集上,MBFusion表现出卓越的性能,与最先进的方法相比,评估指标提高了高达10.1%.
- 废弃性研究证实了个别数据模式和框架模块的贡献.
- 该框架从两种数据类型中实现了平衡和可比的特征表示.
结论:
- 在癌症研究中,MBFusion为多式联络数据融合提供了一个有效的策略.
- 平衡的融合方法显著提高了诊断和预后的预测准确性.
- 解释性分析强调了MBFusion在临床应用和理解癌症机制方面的潜力.
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