摘要
肺癌的病理学图像在预后诊断中起着关键作用,然而,基于未标记像素级别图像进行的肺癌生存分析仍面临诸多挑战,已有的方法往往忽略了临床特征模态的信息、病理学图像块的位置信息以及病理学图像和自然图像的异质性等问题。为了克服这些挑战,提出了一种基于位置感知的多模态肺癌生存预测方法(PSMMSurv)。该方法通过多模态融合和多任务学习有效地利用了病理学图像与临床特征多模态信息。同时,提出的病理学图像特征学习网络可以通过相邻位置的信息交互实现位置感知。此外,通过自监督学习克服了数据异质性问题。在大型肺癌数据集上的实验结果表明,所提方法在C-index这一指标上优于目前已有的方法,能更准确地预测肺癌患者的生存情况,为更好的肺癌预后提供了可靠的支持。
Whole slide images(WSIs)of lung cancer play a pivotal role in prognostic diagnosis.However,survival analysis for lung cancer without pixel-level annotations still encounters numerous challenges.Existing methods often overlook information from clinical feature modalities,spatial information of patches,and the heterogeneity between WSIs and natural images.To address these hurdles,a position-aware based multi-modality lung cancer survival prediction method PSMMSurv is proposed.This approach effectively leverages whole slide images and clinical features through multi-modality fusion and multi-task learning.Furthermore,the proposed whole slide image feature learning network achieves position awareness by interacting with information from adjacent locations.Moreover,data heterogeneity issues are overcome through self-supervised learning.Experimental results on a large lung cancer dataset demonstrate that the proposed method surpasses existing approaches in terms of the C-index metric,enabling more accurate prediction of lung cancer patients’survival outcomes and providing reliable support for better lung cancer prognosis.
作者
王毅诚
宁泰
刘心宇
罗烨
WANG Yicheng;NING Tai;LIU Xinyu;LUO Ye(School of Computer Science and Technology,Tongji University,Shanghai 201804,China)
出处
《计算机科学》
北大核心
2025年第S1期68-75,共8页
Computer Science
基金
国家自然科学基金(62276189)。
关键词
生存分析
病理学影像
多模态融合
自监督学习
多尺度融合
多任务学习
Survival analysis
Whole slide images
Multi-modality fusion
Self-supervised learning
Multi-scale fusion
Multi-task learning
