arXiv: cs.AI preprintsInternational5 November 2026
Multimodal Fusion Methods for Whole-Slide Images and Clinical Data in Inflammatory Bowel Disease
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arXiv:2611.05579v1 Announce Type: cross Abstract: Multimodal AI fusion of histology and clinical data is an active research area in computational pathology, but fusion methods are rarely compared under identical conditions and across different clinical endpoints. We present a modular framework in which slide-level embeddings, obtained by training an attention-based aggregator over patch embeddings from a frozen foundation model, and per-visit clinical embeddings are fed into 15 interchangeable fusion methods with a shared output dimension and prediction head (except decision-level methods). Us
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