Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges. First, they struggle to capture complex cross-modal interactions effectively, which in turn limits performance improvements. Second, they incur high computational costs, restricting their applicability in resource-constrained healthcare AI applications. Finally, they are often designed and evaluated for narrow, fixed modality configurations (e.g., imaging-
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 50%Multi-modal deep learning model for visual acuity prediction from wide field colour fundus imaging - Nature →
- LinkedLinked via arxiv author · 85%Joy Dhar →
“Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention”
- LinkedLinked via arxiv author · 85%Manish Kumar Pandey →
“Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention”
- LinkedLinked via arxiv author · 85%Nayyar Zaidi →
“Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention”
- LinkedLinked via arxiv author · 85%Peter Yichen Chen →
“Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention”
- LinkedLinked via arxiv author · 85%Maryam Haghighat →
“Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention”
- LinkedLinked via arxiv author · 85%Ferdous Sohel →
“Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention”
- LinkedLinked via arxiv author · 85%Puneet Goyal →
“Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention”