提出分层软专家混合模型，提升3D医学图像分割效率与精度
Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
- 分两级路由：局部专家处理区域特征，全局专家融合跨区域信息
- 在三种主流3D医学影像模态上均超越现有最优模型
- 适合需要高效处理复杂医学影像的算法研究者和临床应用开发
近年来，人工智能在医学图像分割领域取得显著进展。然而，跨模态的3D医学图像高效处理及数据异质性问题仍具挑战。本文提出分层软专家混合（Hierarchical Soft Mixture-of-Experts, HoME），一种用于高效长序列建模的双层令牌路由结构，专为3D医学图像分割设计。基于Mamba选择性状态空间模型（SSM）主干网络，HoME通过自适应专家路由增强序列建模能力。第一层采用软专家混合（SMoE）将输入序列划分为局部组，将令牌路由至每组专用专家以提取局部特征；第二层通过全局SMoE聚合输出，实现跨组信息融合与全局上下文优化。该分层设计结合局部专家路由与全局专家精炼，显著提升泛化性与分割性能，在三种最广泛应用的3D医学影像模态及不同数据质量的多个数据集上均超越当前最优结果。代码已公开于 https://github.com/gmum/MambaHoME。
原文摘要 · Abstract (English)
In recent years, artificial intelligence has significantly advanced medical image segmentation. Nonetheless, challenges remain, including efficient 3D medical image processing across diverse modalities and handling data variability. In this work, we introduce Hierarchical Soft Mixture-of-Experts (HoME), a two-level token-routing layer for efficient long-context modeling, specifically designed for 3D medical image segmentation. Built on the Mamba Selective State Space Model (SSM) backbone, HoME enhances sequential modeling through adaptive expert routing. In the first level, a Soft Mixture-of-Experts (SMoE) layer partitions input sequences into local groups, routing tokens to specialized per-group experts for localized feature extraction. The second level aggregates these outputs through a global SMoE layer, enabling cross-group information fusion and global context refinement. This hierarchical design, combining local expert routing with global expert refinement, enhances generalizability and segmentation performance, surpassing state-of-the-art results across datasets from the three most widely used 3D medical imaging modalities and varying data qualities. The code is publicly available at https://github.com/gmum/MambaHoME.
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