Abstract
Weakly supervised semantic segmentation (WSSS) in histopathology seeks to reduce annotation cost by learning from image-level labels, yet it remains limited by inter-class homogeneity, intra-class heterogeneity, and the region-shrinkage effect of CAM-based supervision. We propose a simple and effective prototype-driven framework that leverages vision-language alignment to improve region discovery under weak supervision. Our method integrates CoOp-style learnable prompt tuning to generate text-based prototypes and combines them with learnable image prototypes, forming a dual-modal prototype bank that captures both semantic and appearance cues. To address oversmoothing in ViT representations, we incorporate a multi-scale pyramid module that enhances spatial precision and improves localization quality. Experiments on the BCSS-WSSS benchmark show that our approach surpasses existing state-of-the-art methods, and detailed analyses demonstrate the benefits of text description diversity, context length, and the complementary behavior of text and image prototypes. These results highlight the effectiveness of jointly leveraging textual semantics and visual prototype learning for WSSS in digital pathology.
Abstract (translated)
弱监督语义分割（WSSS）在组织病理学中的目标是通过从图像级别标签学习来减少标注成本，但目前仍受限于类别间的同质性、类别内的异质性以及基于类激活映射（CAM）的监督导致的区域缩小效应。我们提出了一种简单而有效的原型驱动框架，该框架利用视觉-语言对齐以在弱监督下改进区域发现过程。我们的方法整合了CoOp风格的学习提示调整技术来生成文本基元，并将它们与可学习的图像基元相结合，形成了一个双模态基元库，能够捕捉语义和外观线索。为了缓解ViT表示中的过度平滑问题，我们引入了一个多尺度金字塔模块，以增强空间精度并提高定位质量。 在BCSS-WSSS基准上的实验表明，我们的方法超越了现有的最先进技术，并且详细的分析展示了文本描述多样性、上下文长度以及文本与图像基元互补行为的益处。这些结果突显了联合利用文字语义和视觉原型学习对于数字病理学中WSSS的有效性。
URL
https://arxiv.org/abs/2512.10314
