M4oE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Experts

Medical imaging data is inherently heterogeneous across different modalities and clinical centers, posing unique challenges for developing generalizable foundation models. Conventional entail training distinct models per dataset or using a shared encoder with modality-specific decoders. However, these approaches incur heavy computational overheads and suffer from poor scalability. To address these limitations, we propose the Medical Multi-Modal Mixture of Experts (M4oE) framework, leveraging the SwinUNet architecture. Specifically, M4oE comprises modality-specific experts, each separately initialized to learn features encoding domain knowledge. Subsequently, a gating network is integrated during fine-tuning to dynamically modulate each expert’s contribution to the collective predictions. This enhances model interpretability as well as the generalization ability while retaining expertise specialization. Simultaneously, the M4oE architecture amplifies the model’s parallel processing capabilities, and it also ensures the model’s adaptation to new modalities with ease. Experiments across three modalities reveal that M4oE can achieve 3.45% over STU-Net-L, 5.11% over MED3D, and 11.93% over SAM-Med2D across the MICCAI FLARE22, AMOS2022, and ATLAS2023 datasets. Moreover, M4oE showcases a significant reduction in training duration with 7 hours less, while maintaining a parameter count that is only 30% of its compared methods. The code is available at https://github.com/JefferyJiang-YF/M4oE.