标题： ICFNet：用于生存预测的集成跨模态融合网络 显示英文标题

标题： ICFNet: Integrated Cross-modal Fusion Network for Survival Prediction

摘要： 生存预测在医学领域是一项至关重要的任务，对于优化治疗方案和资源配置至关重要。然而，当前的方法通常依赖于有限的数据模态，导致性能不佳。在本文中，我们提出了一种集成跨模态融合网络（ICFNet），该网络整合了组织病理学全切片图像、基因表达谱、患者人口统计信息以及治疗方案。具体来说，采用了三种类型的编码器、残差正交分解模块和统一融合模块来合并多模态特征以提高预测准确性。此外，设计了一个平衡的负对数似然损失函数，以确保不同患者的公平训练。广泛的实验表明，我们的ICFNet在五个公共TCGA数据集（包括BLCA、BRCA、GBMLGG、LUAD和UCEC）上优于最先进的算法，并展示了其支持临床决策和推动精准医学的潜力。代码可在以下网址获取：https://github.com/binging512/ICFNet。 显示英文摘要

摘要： Survival prediction is a crucial task in the medical field and is essential for optimizing treatment options and resource allocation. However, current methods often rely on limited data modalities, resulting in suboptimal performance. In this paper, we propose an Integrated Cross-modal Fusion Network (ICFNet) that integrates histopathology whole slide images, genomic expression profiles, patient demographics, and treatment protocols. Specifically, three types of encoders, a residual orthogonal decomposition module and a unification fusion module are employed to merge multi-modal features to enhance prediction accuracy. Additionally, a balanced negative log-likelihood loss function is designed to ensure fair training across different patients. Extensive experiments demonstrate that our ICFNet outperforms state-of-the-art algorithms on five public TCGA datasets, including BLCA, BRCA, GBMLGG, LUAD, and UCEC, and shows its potential to support clinical decision-making and advance precision medicine. The codes are available at: https://github.com/binging512/ICFNet.