M\textsuperscript{4}Fuse: Lightweight State-Space MoE with a Cross-Scale Gating Bridge for Brain Tumor Segmentation

TL;DR

M extsuperscript{4}Fuse is a lightweight, efficient brain tumor segmentation model that balances encoder-decoder capacity, propagates long-range context, and adapts to cross-site shifts, outperforming existing methods on standard benchmarks.

Contribution

Introduces M extsuperscript{4}Fuse, a novel lightweight network with a cross-scale gating bridge and state space mixer, achieving high accuracy with fewer parameters.

Findings

Outperforms other lightweight models on BraTS benchmarks.

Reduces parameters by over 62% at lower input resolution.

Demonstrates robustness across diverse data sources.

Abstract

Encoder-decoder imbalance and the reliance on large input volumes make many 3D brain tumor segmentation models both compute-heavy and brittle. We present M\textsuperscript{4}Fuse, a lightweight network that prioritizes discriminative brain tumor cues over exhaustive appearance reconstruction. Our method balances encoder and decoder capacity and replaces depth expansion with a synergistic design: it propagates long-range context with linear complexity via a grouped state space mixer, denoises and aligns skip features using a cross-scale dual-stage gating bridge, and absorbs cross-site acquisition shifts with a sample-level mixture-of-experts. On the BraTS2019 and BraTS2021 benchmarks, M\textsuperscript{4}Fuse outperforms other lightweight excellent methods in both parameter count and performance. Even at a challenging input resolution of \(64\times128\times128\) (half that of existing excellent models), M\textsuperscript{4}Fuse reduces parameters by 62.63\% and improves average performance by 0.09\%. Ablations of key components validate the method's exceptional parameter-to-accuracy efficiency and robustness across diverse data centers.