mlsanityData to 7 Oct 2026

MoME: Mixture of Visual Language Medical Experts for Medical Imaging Segmentation

arXiv 2510.26996 ↗First posted 30 Oct 2025cs.CVcited by 0

On the map: Mixture-of-experts → Medical and biomedical MoE

Abstract (arXiv)

In this study, we propose MoME, a Mixture of Visual Language Medical Experts, for Medical Image Segmentation. MoME adapts the successful Mixture of Experts (MoE) paradigm, widely used in Large Language Models (LLMs), for medical vision-language tasks. The architecture enables dynamic expert selection by effectively utilizing multi-scale visual features tailored to the intricacies of medical imagery, enriched with textual embeddings. This work explores a novel integration of vision-language models for this domain. Utilizing an assembly of 10 datasets, encompassing 3,410 CT scans, MoME demonstrates strong performance on a comprehensive medical imaging segmentation benchmark. Our approach explores the integration of foundation models for medical imaging, benefiting from the established efficacy of MoE in boosting model performance by incorporating textual information. Demonstrating competitive precision across multiple datasets, MoME explores a novel architecture for achieving robust results in medical image analysis.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. Builds on 36 of its 51 references that are in the map. First posted on arXiv 30 Oct 2025.

Builds on

36 of 51 references.

References · 36

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24 more
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  2. 2024Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
  3. 2026Dual-Adaptive SAM3: Hierarchical Routing over Low-Rank Expert Layers for Parameter-Efficient Medical Image Segmentation
  4. 2025VLSM-Ensemble: Ensembling CLIP-based Vision-Language Models for Enhanced Medical Image Segmentation
  5. 2023Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models
  6. 2025MedMoE: Modality-Specialized Mixture of Experts for Medical Vision-Language Understanding
  7. 2025MicarVLMoE: A Modern Gated Cross-Aligned Vision-Language Mixture of Experts Model for Medical Image Captioning and Report Generation
  8. 2024On Large Visual Language Models for Medical Imaging Analysis: An Empirical Study