HMAR: Hierarchical Modality-Aware Expert and Dynamic Routing Medical Image Retrieval Architecture
Medical image retrieval (MIR) is a critical component of computer-aided diagnosis, yet existing systems suffer from three persistent limitations: uniform feature encoding that fails to account for the varying clinical importance of anatomical structures, ambiguous similarity metrics based on coarse classification labels, and an exclusive focus on global image similarity that cannot meet the clinical demand for fine-grained region-specific retrieval. We propose HMAR (Hierarchical Modality-Aware Expert and Dynamic Routing), an adaptive retrieval framework built on a Mixture-of-Experts (MoE) architecture. HMAR employs a dual-expert mechanism: Expert0 extracts global features for holistic sim...
| Authors | Aojie Yuan |
|---|---|
| Primary category | cs.CV |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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