Hierarchical Gating Mixture-of-Experts for Mixed-Domain Medical Image Segmentaiton

Learning a unified model that effectively captures domain-specific segmentation patterns remains challenging in mixed-domain medical image segmentation (MD-MIS), where images collected from diverse domains exhibit substantial heterogeneity despite sharing consistent anatomical structures. While Mixture-of-Experts (MoE) holds strong potential for mixed-domain medical image segmentation through expert specialization, existing MoEs suffer from limited feature expressiveness and unstable expert allocation, leading to inadequate domain-discriminative characterization of implicit task heterogeneity and intensified expert competition or collapse. To address the aforementioned challenges, we propose Hierarchical Gating Mixture-of-Experts (HG-MoE), introducing a hierarchical gating framework comprising two complementary mechanisms for effective expert specialization. Firstly, Micro-level Feature Gating is proposed to enhance feature expressiveness through an input-adaptive non-linear gating modulation, thereby enhancing domain-discriminative characterization of implicit task heterogeneity. Secondly, Macro-level Expert Gating is introduced to promote stable task-specific expert allocation through Top-$K$ routing guided by domain-aware expert load distributions, thereby mitigating expert competition and collapse. Extensive experiments on three public datasets across diverse domain scenarios demonstrate the consistent superiority and versatility of HG-MoE. HG-MoE improves Dice by up to $7.30\%$ under the mixed-domain setting, while achieving improvements of $3.04\%$ in Dice and $30.81\%$ in region-level F1 under the single-domain setting, respectively. Source code is publicly available at https://***/HG-MoE.