融合病理、基因等多模态数据，提升癌症生存预测准确率。
ICFNet: Integrated Cross-modal Fusion Network for Survival Prediction
- 整合病理图像、基因表达等多源数据，通过特征融合增强预测能力。
- 在五个TCGA癌症数据集上超越现有方法，显著提升生存预测性能。
- 适合关注精准医疗与临床决策支持的研究者和医生。
生存预测在医学领域至关重要，有助于优化治疗方案和资源分配。然而，现有方法通常仅依赖有限的数据模态，导致性能受限。本文提出集成跨模态融合网络（ICFNet），融合全切片病理图像、基因表达谱、患者人口统计信息及治疗方案。具体采用三种编码器、残差正交分解模块和统一融合模块，以整合多模态特征，提升预测准确性。此外，设计了平衡的负对数似然损失函数，确保不同患者间训练公平性。大量实验表明，ICFNet在五个公开的TCGA数据集（BLCA、BRCA、GBMLGG、LUAD和UCEC）上优于当前最优算法，展现出支持临床决策和推动精准医学的潜力。代码已开源：https://github.com/binging512/ICFNet。
原文摘要 · Abstract (English)
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.
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