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: la
Record details
Published: 13 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026
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ethics.ai (13 June 2026), “Fusion is not one-size-fits-all: Cross-Modal Representation Alignment for Time-to-Event Modeling,” evidence record 1038, https://ethics.ai/record/1038 (originally published by arXiv).
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