Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modeling
Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modality imbalance and distribution shift. We introduce a foundation model-driven framework for cross-modal representation alignment between CT imaging and longitudinal EHR data, designed to generalize across tasks and institutions. CT and EHR modalities are encoded independently using domain-specific foundation models and aligned in a shared latent space through four principled fusion strategies: late fusion, contrastive alignment, cross-attention, and co-attention. We evaluate two clinically distinct TTE tasks: pulmonary embolism (PE) mortality and cardiovascular disease (CVD) outcomes, on l...
| Authors | Zhemin Zhang, Weijie Chen, David Le et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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