Token-Level Cross-Modal Transformer with Contrastive Multi-Task Learning for Breast Cancer Subtype Classification and Survival Prediction
This paper proposes a token-level cross-modal transformer with contrastive multi-task learning to integrate genomic and clinical data for joint breast cancer subtype classification and survival prediction, overcoming limitations of coarse modality interaction, simple fusion, and independent optimization.
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[Submitted on 25 Jun 2026]
Title:Token-Level Cross-Modal Transformer with Contrastive Multi-Task Learning for Breast Cancer Subtype Classification and Survival Prediction
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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.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.16233 [cs.LG]
(or arXiv:2607.16233v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.16233
arXiv-issued DOI via DataCite
Submission history
From: Suxing Liu [view email] [v1] Thu, 25 Jun 2026 03:53:06 UTC (4,764 KB)
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