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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.08356 |
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| _version_ | 1866915336396210176 |
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| author | Chopra, Shivang Sanchez-Rodriguez, Gabriela Mao, Lingchao Feola, Andrew J Li, Jing Kira, Zsolt |
| author_facet | Chopra, Shivang Sanchez-Rodriguez, Gabriela Mao, Lingchao Feola, Andrew J Li, Jing Kira, Zsolt |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08356 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedMoE: Modality-Specialized Mixture of Experts for Medical Vision-Language Understanding Chopra, Shivang Sanchez-Rodriguez, Gabriela Mao, Lingchao Feola, Andrew J Li, Jing Kira, Zsolt Computer Vision and Pattern Recognition 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. |
| title | MedMoE: Modality-Specialized Mixture of Experts for Medical Vision-Language Understanding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.08356 |