用于多模态癌症生存分析的队列-个体协同学习
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis
AI总结：
本文提出队列-个体协同学习框架，通过多模态知识分解和队列引导建模，提升多模态癌症生存分析的判别力与泛化能力，并在五个癌症数据集上验证了有效性。
摘要：
近年来，通过整合多模态数据（例如病理图像和基因组谱），癌症生存分析取得了令人瞩目的进展。然而，这些模态的异质性和高维度给提取具有判别力的表示并同时保持良好的泛化能力带来了重大挑战。在本文中，我们提出了一种队列-个体协同学习（Cohort-individual Cooperative Learning，CCL）框架，通过协同知识分解与队列引导来推进癌症生存分析。具体而言，首先，我们提出了一个多模态知识分解（Multimodal Knowledge Decomposition，MKD）模块，将多模态知识显式分解为四个不同的组成部分：冗余、协同以及两种模态各自的独有信息。这种全面的分解能够启发模型感知容易被忽视但重要的信息，从而促进有效的多模态融合。其次，我们提出了队列引导建模（Cohort Guidance Modeling，CGM），以降低对任务无关信息过拟合的风险。它可以促进对底层多模态数据更全面、更稳健的理解，同时避免过拟合的陷阱，并增强模型的泛化能力。通过协同知识分解和队列引导方法，我们开发了一个具有更强判别能力和泛化能力的稳健多模态生存分析模型。在五个癌症数据集上的大量实验结果表明，我们的模型在整合多模态数据进行生存分析方面具有有效性。
英文摘要：
Recently, we have witnessed impressive achievements in cancer survival analysis by integrating multimodal data, e.g., pathology images and genomic profiles. However, the heterogeneity and high dimensionality of these modalities pose significant challenges for extracting discriminative representations while maintaining good generalization. In this paper, we propose a Cohort-individual Cooperative Learning (CCL) framework to advance cancer survival analysis by collaborating knowledge decomposition and cohort guidance. Specifically, first, we propose a Multimodal Knowledge Decomposition (MKD) module to explicitly decompose multimodal knowledge into four distinct components: redundancy, synergy and uniqueness of the two modalities. Such a comprehensive decomposition can enlighten the models to perceive easily overlooked yet important information, facilitating an effective multimodal fusion. Second, we propose a Cohort Guidance Modeling (CGM) to mitigate the risk of overfitting task-irrelevant information. It can promote a more comprehensive and robust understanding of the underlying multimodal data, while avoiding the pitfalls of overfitting and enhancing the generalization ability of the model. By cooperating the knowledge decomposition and cohort guidance methods, we develop a robust multimodal survival analysis model with enhanced discrimination and generalization abilities. Extensive experimental results on five cancer datasets demonstrate the effectiveness of our model in integrating multimodal data for survival analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
