发表机构
AI VIETNAM Lab; Washington University School of Medicine; Jeonbuk National University; Perelman School of Medicine, University of Pennsylvania（AI越南实验室; 华盛顿大学医学院; 全北国立大学; 宾夕法尼亚大学佩雷尔曼医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ProBAG是一种新的弱监督组织病理学分割方法，通过结合视觉与文本原型、逐类功率重新校准及图扩散机制，在BCSS-WSSS和LUAD-HistoSeg数据集上取得优于现有方法的性能。
AI 中文摘要
弱监督语义分割可通过图像级标注实现组织病理学组织分割，避免了病理专家进行代价高昂的像素级标注。然而，基于类激活图（CAM）的方法通常仅能定位高判别性区域，且在组织界面附近不可靠。我们提出ProBAG，这是一个第一阶段伪掩码生成器，它结合了特定数据集的视觉原型与病理对齐的CONCH文本原型，基于多尺度冻结的UNI特征构建。ProBAG引入了两种互补机制：逐类功率重新校准，其重塑类间竞争的同时保留每个像素处的总前景激活质量；以及一步图扩散，其中特征亲和性被后期Transformer注意力-上下文差异惩罚，该差异用作软结构边界线索。生成的第一阶段伪掩码既不需要条件随机场（CRF）也不需要外部分割模型；为了进行完整的两阶段比较，它们还监督下游的Phikon-FPN分割器。在BCSS-WSSS和LUAD-HistoSeg上的实验表明，与近期的弱监督语义分割（WSSS）方法相比，该方法取得了一致的性能提升，而消融实验显示，病理对齐的文本语义提供了最大的改进，图细化则提供了较小的互补增益。代码可在以下网址获取：this https URL
英文摘要
Weakly supervised semantic segmentation enables histopathology tissue segmentation from image-level annotations, avoiding costly pixel-level labeling by expert pathologists. However, CAM-based methods often localize only highly discriminative regions and remain unreliable near tissue interfaces. We propose ProBAG, a stage-1 pseudo-mask generator that combines dataset-specific visual prototypes with pathology-aligned CONCH text prototypes over multi-scale frozen UNI features. ProBAG introduces two complementary mechanisms: class-wise power recalibration that reshapes inter-class competition while preserving the total foreground activation mass at each pixel, and one-step graph diffusion in which feature affinities are penalized by a late-transformer attention-context discrepancy used as a soft structural boundary cue. The resulting stage-1 pseudo-masks require neither CRF nor an external segmentation model; for complete two-stage comparison, they additionally supervise a downstream Phikon-FPN segmenter. Experiments on BCSS-WSSS and LUAD-HistoSeg show consistent gains over recent WSSS approaches, while ablations indicate that pathology-aligned text semantics provide the largest improvement and graph refinement provides a smaller complementary gain. The code is available at: https://github.com/wterrr/WSSS
Comments12 pages, 2 figures, 4 tables. Accepted by MICCAI Workshop (COMPAYL) 2026
