# MedMoE: Modality-Specialized Mixture of Experts for Medical Vision-Language Understanding

arXiv: https://arxiv.org/abs/2506.08356 · HTML: https://mlsanity.com/paper/2506.08356/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts.
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Placed in

- Mixture-of-experts / [Medical and biomedical MoE](https://mlsanity.com/moe/medical-and-biomedical-moe/)

## Builds on (most cited first, 17 of 17)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://mlsanity.com/paper/2112.06905/) (2021)
- [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs](https://mlsanity.com/paper/2303.00915/) (2023)
- [MoE-LLaVA: Mixture of Experts for Large Vision-Language Models](https://mlsanity.com/paper/2401.15947/) (2024)
- [PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents](https://mlsanity.com/paper/2303.07240/) (2023)
- [Quilt-1M: One Million Image-Text Pairs for Histopathology](https://mlsanity.com/paper/2306.11207/) (2023)
- [Advancing Radiograph Representation Learning with Masked Record Modeling](https://mlsanity.com/paper/2301.13155/) (2023)
- [UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities](https://mlsanity.com/paper/2412.10372/) (2024)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [MM-Retinal: Knowledge-Enhanced Foundational Pretraining with Fundus Image-Text Expertise](https://mlsanity.com/paper/2405.11793/) (2024)

## Built on by (most cited first, 10 of 10)

- [Mixture of Experts in Large Language Models](https://mlsanity.com/paper/2507.11181/) (2025)
- [MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution](https://mlsanity.com/paper/2604.26283/) (2026)
- [Generalizable Operating Room Expert with Multimodal Enhancement](https://mlsanity.com/paper/2508.08199/) (2025)
- [REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis](https://mlsanity.com/paper/2510.04923/) (2025)
- [The Rise of Sparse Mixture-of-Experts: A Survey from Algorithmic Foundations to Decentralized Architectures and Vertical Domain Applications](https://mlsanity.com/paper/2602.08019/) (2026)
- [M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Model](https://mlsanity.com/paper/2604.08936/) (2026)
- [Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models](https://mlsanity.com/paper/2605.21861/) (2026)
- [Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey](https://mlsanity.com/paper/2605.27431/) (2026)
- [MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification](https://mlsanity.com/paper/2607.29462/) (2026)
- [Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging](https://mlsanity.com/paper/2609.18688/) (2026)

## Abstract (arXiv)

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.