分离染色与结构特征，用少量标注数据实现病理图像精准分割
Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation
- 设计双学生网络，分别学习染色变化和组织结构信息
- 在5%标注数据下，GlaS和CRAG数据集的分割效果提升1.2%和0.7%
- 适合资源有限的医学图像分析场景，尤其关注少样本分割
准确分割病理图像中的腺体对癌症诊断与预后至关重要。然而，苏木精-伊红（H&E）染色差异大、组织形态复杂，加之标注数据稀缺，给自动分割带来挑战。为此，我们提出颜色-结构双学生（CSDS）框架，旨在学习染色外观与组织结构的解耦表征。CSDS包含两个专用学生网络：一个在染色增强输入上训练以建模色彩变异，另一个在结构增强输入上训练以捕捉形态线索。共享教师网络通过指数移动平均（EMA）更新，利用伪标签监督两个学生。为提升标签可靠性，引入染色感知与结构感知不确定性估计模块，动态调节学生贡献。在GlaS与CRAG数据集上的实验表明，CSDS在低标注设置下达到当前最优性能，在5%标注数据下，GlaS和CRAG的Dice分数分别提升1.2%和0.7%，10%标注下分别提升0.7%和1.4%。代码与预训练模型已开源。
原文摘要 · Abstract (English)
Accurate gland segmentation in histopathology images is essential for cancer diagnosis and prognosis. However, significant variability in Hematoxylin and Eosin (H&E) staining and tissue morphology, combined with limited annotated data, poses major challenges for automated segmentation. To address this, we propose Color-Structure Dual-Student (CSDS), a novel semi-supervised segmentation framework designed to learn disentangled representations of stain appearance and tissue structure. CSDS comprises two specialized student networks: one trained on stain-augmented inputs to model chromatic variation, and the other on structure-augmented inputs to capture morphological cues. A shared teacher network, updated via Exponential Moving Average (EMA), supervises both students through pseudo-labels. To further improve label reliability, we introduce stain-aware and structure-aware uncertainty estimation modules that adaptively modulate the contribution of each student during training. Experiments on the GlaS and CRAG datasets show that CSDS achieves state-of-the-art performance in low-label settings, with Dice score improvements of up to 1.2% on GlaS and 0.7% on CRAG at 5% labeled data, and 0.7% and 1.4% at 10%. Our code and pre-trained models are available at https://github.com/hieuphamha19/CSDS.
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