Abstract
Weakly supervised semantic segmentation (WSSS) in histopathology relies heavily on classification backbones, yet these models often localize only the most discriminative regions and struggle to capture the full spatial extent of tissue structures. Vision-language models such as CONCH offer rich semantic alignment and morphology-aware representations, while modern segmentation backbones like SegFormer preserve fine-grained spatial cues. However, combining these complementary strengths remains challenging, especially under weak supervision and without dense annotations. We propose a prototype learning framework for WSSS in histopathological images that integrates morphology-aware representations from CONCH, multi-scale structural cues from SegFormer, and text-guided semantic alignment to produce prototypes that are simultaneously semantically discriminative and spatially coherent. To effectively leverage these heterogeneous sources, we introduce text-guided prototype initialization that incorporates pathology descriptions to generate more complete and semantically accurate pseudo-masks. A structural distillation mechanism transfers spatial knowledge from SegFormer to preserve fine-grained morphological patterns and local tissue boundaries during prototype learning. Our approach produces high-quality pseudo masks without pixel-level annotations, improves localization completeness, and enhances semantic consistency across tissue types. Experiments on BCSS-WSSS datasets demonstrate that our prototype learning framework outperforms existing WSSS methods while remaining computationally efficient through frozen foundation model backbones and lightweight trainable adapters.
Abstract (translated)
在组织病理学中，弱监督语义分割（WSSS）主要依赖于分类骨干模型。然而，这些模型通常只能定位最具判别力的区域，并且难以捕捉到组织结构的整体空间范围。视觉-语言模型如CONCH提供了丰富的语义对齐和形态感知表示，而现代分割骨架如SegFormer则保留了精细的空间线索。但是，在弱监督条件下以及缺乏密集注释的情况下，将这些互补优势结合在一起仍然存在挑战。 我们提出了一种用于组织病理图像WSSS的原型学习框架，该框架整合了来自CONCH的形态感知表示、SegFormer的多尺度结构提示和文本引导语义对齐，以生成同时具有判别性和空间一致性的原型。为了有效利用这些异质资源，我们引入了基于病理描述的文本引导原型初始化方法，从而生成更加完整且语义准确的伪掩膜（pseudo-masks）。此外，一种结构蒸馏机制从SegFormer中传输空间知识，在原型学习过程中保留细粒度形态模式和局部组织边界。我们的方法能够在没有像素级注释的情况下生成高质量的伪掩膜，提高定位完整性，并增强不同组织类型之间的语义一致性。 在BCSS-WSSS数据集上的实验表明，与现有的WSSS方法相比，我们提出的原型学习框架不仅性能更优，而且由于冻结了基础模型骨干和轻量可训练适配器的应用，使其计算效率更高。
URL
https://arxiv.org/abs/2512.10316
