multi-modal-learning

This paper examines the integration of multi-modal clinical data, including treatment records, pathology reports, and clinician notes, using rule-based extraction and machine learning to improve breast cancer recurrence prediction compared to single-modal approaches.

This paper proposes M-Hyper, a novel multi-modal knowledge graph completion method that balances fusion and independence of modality representations using hypercomplex (biquaternion) algebra. The approach introduces Fine-grained Entity Representation Factorization and Robust Relation-aware Modality Fusion modules to achieve state-of-the-art performance with improved robustness.