Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
In recent years, artificial intelligence has significantly advanced medical
image segmentation. However, 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 state-space
model (SSM) backbone, HoME enhances sequential modeling through sparse,
adaptive expert routing. The first stage employs a Soft Mixture-of-Experts
(SMoE) layer to partition input sequences into local groups, routing tokens to
specialized per-group experts for localized feature extraction. The second
stage aggregates these outputs via a global SMoE layer, enabling cross-group
information fusion and global context refinement. This hierarchical design,
combining local expert routing with global expert refinement improves
generalizability and segmentation performance, surpassing state-of-the-art
results across datasets from the three most commonly used 3D medical imaging
modalities and data quality.
