用原型提示提升病理图像弱监督分割精度
Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation
- 基于聚类构建每类多个原型，捕捉类别内差异
- 对比学习匹配特征与原型，生成更完整的分割图
- 在4个数据集上超越现有方法，适合医学图像分析
弱监督图像分割在仅有图像级标签的情况下受到关注，因像素级标注成本高昂。传统基于类别激活图（CAM）的方法常只聚焦最判别性区域，导致掩码不完整。近期引入文本信息的方法在病理图像上表现不佳，因其存在类间相似性高、类内异质性强的问题。本文提出一种基于原型的图像提示框架，通过聚类从训练集构建图像库，为每类提取多个原型特征以捕捉类内多样性。设计输入特征与类别特定原型间的匹配损失，利用对比学习缓解类间同质性问题，引导模型生成更准确的CAM。在四个数据集（LUAD-HistoSeg、BCSS-WSSS、GCSS和BCSS）上的实验表明，该方法优于现有弱监督分割方法，树立了新基准。
原文摘要 · Abstract (English)
Weakly supervised image segmentation with image-level labels has drawn attention due to the high cost of pixel-level annotations. Traditional methods using Class Activation Maps (CAMs) often highlight only the most discriminative regions, leading to incomplete masks. Recent approaches that introduce textual information struggle with histopathological images due to inter-class homogeneity and intra-class heterogeneity. In this paper, we propose a prototype-based image prompting framework for histopathological image segmentation. It constructs an image bank from the training set using clustering, extracting multiple prototype features per class to capture intra-class heterogeneity. By designing a matching loss between input features and class-specific prototypes using contrastive learning, our method addresses inter-class homogeneity and guides the model to generate more accurate CAMs. Experiments on four datasets (LUAD-HistoSeg, BCSS-WSSS, GCSS, and BCSS) show that our method outperforms existing weakly supervised segmentation approaches, setting new benchmarks in histopathological image segmentation.
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