分而治之与聚合：用于类别不平衡半监督医学图像分割的非对称专家
Divide, Conquer, and Aggregate: Asymmetric Experts for Class-Imbalanced Semi-Supervised Medical Image Segmentation
针对类别不平衡的半监督医学图像分割，提出非对称专家解码器与动态特征聚合模块，可复用于多器官分割中的尾类提升与伪标签质量优化。
原文依据 we propose a "Divide, Conquer, and Aggregate" (DCA) framework, featuring a unified encoder, three expert decoders, and an aggregation decoder.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论半监督医学图像分割（SSMIS）旨在缓解标注稀缺问题，但通用方法通常在少类数据集上开发，在类别不平衡的多器官场景中性能下降。现有的类别不平衡SSMIS方法也面临困难，因为其单解码器架构被迫以共享参数处理差异巨大的尺度。这一过程容易被多数类主导，从根本上限制了尾类分割能力。为解决此问题，我们提出“分而治之与聚合”（DCA）框架，包含统一编码器、三个专家解码器和聚合解码器。首先，我们通过应用对数差距分析进行划分，将前景类静态划分为稳定的头部、中部和尾部集合，这与解剖先验一致。然后，我们通过使用标签拆分策略独立训练三个架构非对称的专家进行征服。这从根本上减轻了单个解码器的负担。专家在无标注数据上的预测通过logit拼接融合，以生成高质量伪标签。最后，我们使用带有动态特征聚合模块（DFAM）的聚合解码器进行聚合，该模块动态融合所有三个专家的先验，以实现无偏预测并充分利用无标注数据。实验表明，我们的DCA框架显著优于最优的通用和类别不平衡SSMIS方法。
英文原文摘要 1482 字符
Semi-supervised medical image segmentation (SSMIS) aims to alleviate annotation scarcity, but general methods, often developed on few-class datasets, suffer performance degradation in class-imbalanced multi-organ scenarios. Existing class-imbalanced SSMIS methods also struggle, as their single-decoder architecture is forced to handle vastly different scales with shared parameters. This process is easily dominated by majority classes, fundamentally limiting tail-class segmentation capability. To address this, we propose a "Divide, Conquer, and Aggregate" (DCA) framework, featuring a unified encoder, three expert decoders, and an aggregation decoder. First, we Divide by applying a Logarithmic Gap Analysis to statically partition foreground classes into stable Head, Medium, and Tail sets, which aligns with anatomical priors. Then, we Conquer by training the three architecturally asymmetric experts independently using a label-split strategy. This fundamentally alleviates the burden on a single decoder. The experts' predictions on unlabeled data are fused via logit stitching to generate high-quality pseudo-labels. Finally, we Aggregate using an aggregation decoder with a Dynamic Feature Aggregation Module (DFAM), which dynamically fuses priors from all three experts to achieve unbiased predictions and fully leverage unlabeled data. Experiments demonstrate that our DCA framework significantly outperforms state-of-the-art general and class-imbalanced SSMIS methods.