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

Abstract: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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.16233 [cs.LG]

(or arXiv:2607.16233v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.16233 Focus to learn more arXiv-issued DOI via DataCite

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