反事实双向协同注意力变压器用于整合性组织学-基因组癌症风险分层
IEEE journal of biomedical and health informatics
|March 5, 2025
概括
这项研究引入了一种新的深度学习框架,用于使用组织学和基因组数据预测患者的存活率. 新模型通过减少偏差和增强特征相互作用,显著提高了预后准确性.
科学领域:
- 计算病理学计算病理学
- 生物信息学是一种生物信息学.
- 机器学习在瘤学中
背景情况:
- 根据组织学全片图像 (WSIs) 和基因组数据预测患者的存活率是复杂的,因为瘤异质.
- 当前的深度学习方法面临着来自不完整或无关紧要的基因组指导的学习偏差的挑战,导致不理想的预后预测.
研究的目的:
- 开发一个先进的深度学习框架,有效地整合组织学和基因组数据,以改善患者预后生存预测.
- 解决和减轻现有的多式模式癌症数据分析方法固有的学习偏差.
主要方法:
- 提出了反事实双向同时注意力变压器 (CFBCT) 框架.
- 集成了一个双向的共同注意层,以增强基因组和组织学数据之间的特征相互作用.
- 采用反事实推理和因果模型来分层癌症风险和减少偏见.
主要成果:
- CFBCT框架在来自癌症基因组图谱 (TCGA) 的八个不同癌症基准数据集中表现出卓越的性能.
- 与18个最先进的模型相比,在患者预后预测中,c指数表现平均有2.5%的改善.
- 展示了有效的特征相互作用,并从WSIs中一致识别了预后特征.
结论:
- 拟议的CFBCT框架为患者生存预测提供了与现有的组织学-基因组学习方法相比的显著进步.
- 因果和反事实方法提高了对生存结果特征影响的理解,并减少了模型偏差.
- 该框架在多种癌症类型的强有力的验证强调了其临床应用的潜力.
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