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University of North Carolina at Chapel Hill (unc
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Explicitly linking multimodal patient data to biomedical knowledge graphs unlocks significant gains for clinical LLMs, with performance plummeting when these crucial connections are removed.
Rigid coarse-to-fine hierarchies cripple multi-scale anomaly detection: letting arbitrary patch granularities interact symmetrically via bidirectional cross-scale attention yields a 9%+ VUS-PR leap over 50 baselines across 530 time series benchmarks.
PathMoE reveals the specific modality interactions driving individual predictions in pediatric brain tumor classification, offering crucial interpretability for rare tumor subtypes.
