用专家分工+动态融合，提升病理图像少样本分割精度。
Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
- 三路专家网络分别处理分割、边界和距离场，各司其职。
- 在GlaS和CRAG数据集上，仅用10%标签即超越现有方法。
- 自动平衡多目标损失，无需人工调参，适合医学图像场景。
半监督学习被用于缓解组织病理图像分割对大量标注数据的依赖，但现有方法因腺体边界模糊和形态误判导致伪标签噪声问题严重。本文提出Semi-MOE，据我们所知首个面向半监督病理图像分割的多任务混合专家框架。该方法包含三个专用专家网络：主分割专家、符号距离场回归专家与边界预测专家，分别捕捉不同形态特征。随后，多门控伪标签模块动态聚合专家特征，实现鲁棒的融合-精炼伪标签机制。为消除人工调参并动态平衡多个学习目标，我们提出自适应多目标损失函数。在GlaS和CRAG基准上的大量实验表明，本方法在低标签设置下显著优于现有最先进方法，凸显了基于MoE架构在半监督分割中的潜力。代码已开源：https://github.com/vnlvi2k3/Semi-MoE。
原文摘要 · Abstract (English)
Semi-supervised learning has been employed to alleviate the need for extensive labeled data for histopathology image segmentation, but existing methods struggle with noisy pseudo-labels due to ambiguous gland boundaries and morphological misclassification. This paper introduces Semi-MOE, to the best of our knowledge, the first multi-task Mixture-of-Experts framework for semi-supervised histopathology image segmentation. Our approach leverages three specialized expert networks: A main segmentation expert, a signed distance field regression expert, and a boundary prediction expert, each dedicated to capturing distinct morphological features. Subsequently, the Multi-Gating Pseudo-labeling module dynamically aggregates expert features, enabling a robust fuse-and-refine pseudo-labeling mechanism. Furthermore, to eliminate manual tuning while dynamically balancing multiple learning objectives, we propose an Adaptive Multi-Objective Loss. Extensive experiments on GlaS and CRAG benchmarks show that our method outperforms state-of-the-art approaches in low-label settings, highlighting the potential of MoE-based architectures in advancing semi-supervised segmentation. Our code is available at https://github.com/vnlvi2k3/Semi-MoE.
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