Abstract
Weakly supervised semantic segmentation (WSSS) in histopathology reduces pixel-level labeling by learning from image-level labels, but it is hindered by inter-class homogeneity, intra-class heterogeneity, and CAM-induced region shrinkage (global pooling-based class activation maps whose activations highlight only the most distinctive areas and miss nearby class regions). Recent works address these challenges by constructing a clustering prototype bank and then refining masks in a separate stage; however, such two-stage pipelines are costly, sensitive to hyperparameters, and decouple prototype discovery from segmentation learning, limiting their effectiveness and efficiency. We propose a cluster-free, one-stage learnable-prototype framework with diversity regularization to enhance morphological intra-class heterogeneity coverage. Our approach achieves state-of-the-art (SOTA) performance on BCSS-WSSS, outperforming prior methods in mIoU and mDice. Qualitative segmentation maps show sharper boundaries and fewer mislabels, and activation heatmaps further reveal that, compared with clustering-based prototypes, our learnable prototypes cover more diverse and complementary regions within each class, providing consistent qualitative evidence for their effectiveness.
Abstract (translated)
弱监督语义分割（WSSS）在组织病理学中通过从图像级别标签学习来减少像素级标注，但该方法受到类内异质性、类间同质性和CAM诱导的区域收缩等问题的影响。最近的研究通过构建聚类原型库并在独立阶段进行掩码细化来解决这些问题；然而，这类两阶段管道成本高，对超参数敏感，并且将原型发现与分割学习解耦，限制了其有效性和效率。我们提出了一种无需集群的一阶段可学习原型框架，并采用多样性正则化以增强形态类内异质性的覆盖范围。我们的方法在BCSS-WSSS数据集上达到了最先进的（SOTA）性能，在mIoU和mDice指标上超越了以前的方法。定性分割图显示出了更清晰的边界并且错误标注较少，而激活热图进一步揭示了相比于基于聚类的原型，我们的可学习原型能够覆盖每个类别内更多样化且互补的区域，提供了其有效性的持续证据。
URL
https://arxiv.org/abs/2512.05922
