轻量级脑肿瘤分割模型，用高效结构提升精度与鲁棒性
M\textsuperscript{4}Fuse: Lightweight State-Space MoE with a Cross-Scale Gating Bridge for Brain Tumor Segmentation
- 用跨尺度门控桥融合特征，提升细节保留能力
- 在64×128×128输入下参数减少62.63%，平均性能提升0.09
- 适合资源受限场景下的医学图像分割任务
编码器-解码器容量失衡和对大输入体积的依赖，使许多3D脑肿瘤分割模型计算量大且脆弱。我们提出M⁴Fuse，一种轻量级网络，优先关注具有判别性的脑肿瘤特征而非全面外观重建。该方法平衡编码器与解码器容量，以协同设计替代深度扩展：通过分组状态空间混频器以线性复杂度传播长程上下文，利用跨尺度双阶段门控桥去噪并对齐跳跃连接特征，并通过样本级混合专家机制适应不同采集站点的差异。在BraTS2019和BraTS2021基准上，M⁴Fuse在参数量和性能方面均优于其他轻量级优秀方法。即使在64×128×128的挑战性输入分辨率下（为现有优秀模型的一半），其参数量减少62.63%，平均性能提升0.09。关键组件消融实验验证了该方法在参数-精度效率与跨中心数据鲁棒性方面的卓越表现。
原文摘要 · Abstract (English)
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.
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