Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention
Merged summary
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.
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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.