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MedMoE: Modality-Specialized Mixture of Experts for Medical Vision-Language Understanding
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MedMoE: Modality-Specialized Mixture of Experts for Medical Vision-Language Understanding
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Different medical imaging modalities capture diagnostic information at varying spatial resolutions, from coarse global patterns to fine-grained localized structures. However, most existing vision-language frameworks in the medical domain apply a uniform strategy for local feature extraction, overlooking the modality-specific demands. In this work, we present MedMoE, a modular and extensible vision-language processing framework that dynamically adapts visual representation based on the diagnostic context. MedMoE incorporates a Mixture-of-Experts (MoE) module conditioned on the report type, which routes multi-scale image features through specialized expert branches trained to capture modality-specific visual semantics. These experts operate over feature pyramids derived from a Swin Transformer backbone, enabling spatially adaptive attention to clinically relevant regions. This framework produces localized visual representations aligned with textual descriptions, without requiring modality-specific supervision at inference. Empirical results on diverse medical benchmarks demonstrate that MedMoE improves alignment and retrieval performance across imaging modalities, underscoring the value of modality-specialized visual representations in clinical vision-language systems.
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Cited by 7 Pith papers
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V proposes meta-query prior memorization, causal counterfactual refinement via RL, and dual-branch memory transition to evolve implicit diagnostic memories in medical VLMs and boost accuracy over chain-of-t...
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V evolves latent diagnostic memories via meta queries, causal counterfactual refinement with RL, and dual-branch memory transition to outperform prior medical VLM methods in diagnostic accuracy.
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Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey
A literature survey that categorizes how Mixture-of-Experts architectures address multimodal learning challenges and identifies open research gaps.
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Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models
DEX is a modular network using dynamically activated experts and a group-EMA director to learn emergent modular representations for multi-modality medical vision foundation models, evaluated on a new 4M-image benchmar...
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V proposes a latent diagnostic memory evolution framework using Meta Query, Causal Counterfactual Refinement, and Intrinsic Memory Transition to improve medical VLM diagnostic accuracy over chain-of-thought...
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M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Model
M-IDoL learns modality-specific and diverse representations by maximizing inter-modality entropy and minimizing intra-modality uncertainty through information decomposition in MoE subspaces.
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V proposes a latent memory evolution framework with meta-query prior retrieval, causal counterfactual refinement via RL, and intrinsic memory transition to improve diagnostic accuracy over chain-of-thought ...
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