通过自适应原型学习，减少多模态癌症生存分析中的冗余信息。
Adaptive Prototype Learning for Multimodal Cancer Survival Analysis
- 用可学习的查询向量自适应提取代表性原型，降低数据冗余。
- 在五个癌症数据集上优于现有方法，提升生存预测准确性。
- 适合关注多模态医学图像与基因表达融合的研究者。
利用多模态数据，特别是全切片组织学图像（WSIs）与转录组谱型的整合，有望提升癌症生存预测性能。然而，多模态数据中的过度冗余会降低模型表现。本文提出自适应原型学习（APL），一种新颖有效的多模态癌症生存分析方法。APL以数据驱动方式自适应学习代表性原型，减少冗余同时保留关键信息。该方法采用两组可学习查询向量，作为高维表示与生存预测之间的桥梁，捕捉任务相关特征。此外，引入多模态混合自注意力机制，促进跨模态交互，进一步增强信息融合。在五个基准癌症数据集上的大量实验表明，本方法优于现有方法。代码已公开于 https://github.com/HongLiuuuuu/APL。
原文摘要 · Abstract (English)
Leveraging multimodal data, particularly the integration of whole-slide histology images (WSIs) and transcriptomic profiles, holds great promise for improving cancer survival prediction. However, excessive redundancy in multimodal data can degrade model performance. In this paper, we propose Adaptive Prototype Learning (APL), a novel and effective approach for multimodal cancer survival analysis. APL adaptively learns representative prototypes in a data-driven manner, reducing redundancy while preserving critical information. Our method employs two sets of learnable query vectors that serve as a bridge between high-dimensional representations and survival prediction, capturing task-relevant features. Additionally, we introduce a multimodal mixed self-attention mechanism to enable cross-modal interactions, further enhancing information fusion. Extensive experiments on five benchmark cancer datasets demonstrate the superiority of our approach over existing methods. The code is available at https://github.com/HongLiuuuuu/APL.
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