通过细粒度跨模态交互提升乳腺癌分型与生存预测精度

Token-Level Cross-Modal Transformer with Contrastive Multi-Task Learning for Breast Cancer Subtype Classification and Survival Prediction

• 设计令牌级跨模态变换器，实现基因组与临床数据的细粒度对齐
• 在METABRIC数据集上，分型准确率达87.3%，生存预测C-index达0.76
• 适合关注多模态融合与精准肿瘤学的科研与临床工作者

整合异构基因组与临床模态以联合进行癌症分型与生存预测，仍是精准肿瘤学中的关键挑战。现有方法存在三方面局限：(1) 将每种模态视为单一特征向量，无法实现细粒度的跨模态令牌级交互；(2) 跨模态融合通常采用线性加权或后期平均，缺乏结构化令牌交换机制；(3) 生存预测与分类目标独立优化，缺失联合正则化信号。本文提出一种基于对比多任务学习的令牌级跨模态变压器（Token-Level Cross-Modal Transformer, TLCMT），在多个数据集上验证其有效性，尤其在乳腺癌分型与生存预测任务中表现优异。

原文摘要 · Abstract (English)

Integrating heterogeneous genomic and clinical modalities for joint cancer subtype classification and survival prediction remains a key challenge in precision oncology. Existing approaches suffer from three limitations: (1) they treat each modality as a monolithic feature vector, precluding fine-grained token-level interactions across modalities; (2) cross-modal fusion is typically performed through linear weighting or late averaging rather than structured token exchange; and (3) survival and classification objectives are optimized independently, missing a joint regularization signal.

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。