HSFSurv:用于多模式癌症生存分析的个体和特征水平的混合监督框架

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Bangkang Fu , Junjie He , Xiaoli Zhang , Yunsong Peng , Zhuxu Zhang , Qi Tang , Xinfeng Liu , Ying Cao , Rongpin Wang
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引用次数: 0

摘要

多模态数据在生存分析中发挥着重要作用,病理图像提供肿瘤的形态学信息,基因组数据提供分子洞察力。利用多模态数据进行生存分析已成为一个突出的研究课题。然而,数据的异质性给多模态集成带来了重大挑战。虽然现有的方法考虑了来自不同模态的特征之间的相互作用,但特征空间的异质性往往阻碍了生存分析的性能。本文提出了一种基于多模态特征分解的混合监督生存分析框架(HSFSurv)。该框架利用多模态特征分解模块将特征划分为高度相关和模态特定的组件,便于后续步骤中有针对性的特征融合。为了缓解特征空间的异质性,我们设计了一个个体层面的不确定性最小化(UMI)模块,以确保预测结果的一致性。此外,我们开发了一个特征级多模态队列对比学习(MCF)模块,以加强特征之间的一致性。此外,还引入了带有监控信号的概率衰减检测模块来指导对比学习过程。这些模块被联合训练以将多模态特征投影到共享的潜在向量空间中。最后,我们对生存分析任务的框架进行微调,以实现预后预测。在五个癌症数据集上的实验结果证明了所提出的多模式融合框架在生存分析中的最先进性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HSFSurv: A hybrid supervision framework at individual and feature levels for multimodal cancer survival analysis
Multimodal data play a significant role in survival analysis, with pathological images providing morphological information about tumors and genomic data offering molecular insights. Leveraging multimodal data for survival analysis has become a prominent research topic. However, the heterogeneity of data poses significant challenges to multimodal integration. While existing methods consider interactions among features from different modalities, the heterogeneity of feature spaces often hinders performance in survival analysis. In this paper, we propose a hybrid supervised framework for survival analysis (HSFSurv) based on multimodal feature decomposition. This framework utilizes a multimodal feature decomposition module to partition features into highly correlated and modality-specific components, facilitating targeted feature fusion in subsequent steps. To alleviate feature space heterogeneity, we design an individual-level uncertainty minimization (UMI) module to ensure consistency in prediction outcomes. Additionally, we develop a feature-level multimodal cohort contrastive learning (MCF) module to enforce consistency across features. Moreover, a probabilistic decay detection module with a supervisory signal is introduced to guide the contrastive learning process. These modules are jointly trained to project multimodal features into a shared latent vector space. Finally, we fine-tune the framework for survival analysis tasks to achieve prognostic predictions. Experimental results on five cancer datasets demonstrate the state-of-the-art performance of the proposed multimodal fusion framework in survival analysis.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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