Cancer survival analysis commonly integrates information across diverse medical modalities to make survival-time predictions. Existing methods primarily focus on extracting different decoupled features of modalities and performing fusion operations such as concatenation, attention, and \revm{Mixture-of-Experts (MoE)-based fusion. However, these methods still face two key challenges: i) Fixed fusion schemes (concatenation and attention) can lead to model over-reliance on predefined feature combinations, limiting the dynamic fusion of decoupled features; ii) in MoE-based fusion methods, each expert network handles separate decoupled features, which limits information interaction among the decoupled features. To address these challenges, we propose a novel Decoupling-Reorganization-Fusion framework (DeReF), which devises a random feature reorganization strategy between modalities decoupling and dynamic MoE fusion modules.Its advantages are: i) it increases the diversity of feature combinations and granularity, enhancing the generalization ability of the subsequent expert networks; ii) it overcomes the problem of information closure and helps expert networks better capture information among decoupled features. Additionally, we incorporate a regional cross-attention network within the modality decoupling module to improve the representation quality of decoupled features. Extensive experimental results on our in-house Liver Cancer (LC) and three widely used TCGA public datasets confirm the effectiveness of our proposed method. Codes are available at https://github.com/ZJUMAI/DeReF.

翻译：癌症生存分析通常整合多种医学模态的信息以进行生存时间预测。现有方法主要侧重于提取模态的不同解耦特征，并执行诸如拼接、注意力以及基于专家混合（MoE）的融合操作。然而，这些方法仍面临两个关键挑战：i) 固定融合方案（如拼接和注意力）可能导致模型过度依赖预定义的特征组合，限制了解耦特征的动态融合；ii) 在基于MoE的融合方法中，每个专家网络处理独立的解耦特征，这限制了解耦特征间的信息交互。为解决这些挑战，我们提出了一种新颖的解耦-重组-融合框架（DeReF），该框架在模态解耦与动态MoE融合模块之间设计了一种随机特征重组策略。其优势在于：i) 增加了特征组合与粒度的多样性，增强了后续专家网络的泛化能力；ii) 克服了信息封闭问题，有助于专家网络更好地捕捉解耦特征间的信息。此外，我们在模态解耦模块中引入了一个区域交叉注意力网络，以提升解耦特征的表征质量。在我们内部构建的肝癌（LC）数据集以及三个广泛使用的TCGA公共数据集上进行的大量实验结果证实了所提方法的有效性。代码可在 https://github.com/ZJUMAI/DeReF 获取。