用于乳腺癌亚型分类和生存预测的具有对比多任务学习的令牌级跨模态变换器
Token-Level Cross-Modal Transformer with Contrastive Multi-Task Learning for Breast Cancer Subtype Classification and Survival Prediction
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中文总结 AI 辅助
针对整合异质模态进行癌症亚型分类和生存预测的挑战，提出令牌级跨模态变换器及对比多任务学习方法，克服现有方法在模态交互、融合方式及目标优化上的局限。
中文摘要 AI 辅助
整合异质基因组和临床模态进行联合癌症亚型分类和生存预测仍是精准肿瘤学的关键挑战。现有方法有三个局限性：将各模态视为整体特征向量，排除模态间细粒度令牌级交互；跨模态融合通常通过线性加权或后期平均而非结构化令牌交换；生存和分类目标独立优化，缺少联合正则化信号。
英文摘要
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.
发表机构
- Jiangxi Arts & Ceramics Technology Institute（江西艺术陶瓷科技职业学院）
- Mokwon University（木浦大学）
机构由 AI 辅助整理，请以论文原文为准。
