Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention

TL;DR - CURE is a lightweight, scalable framework for progressively fusing heterogeneous medical data using hybrid geometry-aware attention. Across 16 public datasets, it improved performance by up to 3.97% while reducing computational costs by up to 87.8%.

• Sequential HyFuse layers support varying combinations of imaging, clinical, and omics data.
• Residual convolutions capture multi-scale features, while attention mixes coarse-to-fine structural cues.
• Late fusion and shared-information refinement produce modality-order-invariant representations.
• The design targets both cross-modal modeling quality and resource-constrained deployment.