构建可持续更新的多模态模型，提升癌症预后预测准确率
Continually Evolved Multimodal Foundation Models for Cancer Prognosis
- 设计持续演化的多模态框架，支持新数据动态融入训练
- 在TCGA数据集上显著优于传统方法，实现更稳健的生存预测
- 适合临床研究者与医疗AI开发者参考应用
癌症预后是预测患者结局和生存率的关键任务。为提升预测精度，以往研究整合了临床文本、医学影像和基因组数据等多模态信息，利用其互补性。然而，现有方法存在两大局限：一是难以将分布不同的新数据（如不同医院的病历）有效纳入训练，导致泛化能力不足；二是多数多模态融合方法依赖简单拼接或特定任务流程，无法捕捉模态间的复杂关联。为此，我们提出一种持续演化的多模态基础模型。在TCGA数据集上的大量实验验证了该方法的有效性，表明其具备实现鲁棒且自适应多模态融合的潜力，有望推动癌症预后预测的发展。
原文摘要 · Abstract (English)
Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. To enhance prediction accuracy, previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information. However, existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals, thus rendering sub-optimal generalizability and limited utility in real-world applications. Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities. To address these, we propose a continually evolving multi-modal foundation model. Extensive experiments on the TCGA dataset demonstrate the effectiveness of our approach, highlighting its potential to advance cancer prognosis by enabling robust and adaptive multimodal integration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。