Abstract
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.
Abstract (translated)
弱监督下的图像分割，特别是仅使用图像级标签的情况，因像素级标注成本高昂而受到了广泛关注。传统的基于类激活图（CAMs）的方法往往只能突出最具辨识力的区域，导致生成的掩码不完整。近期引入文本信息以改进分割效果的方法，在处理组织病理学图像时遇到了类别间同质性和类别内异质性的问题。 本文提出了一种用于组织病理学图像分割的基于原型的图像提示框架。该方法通过聚类训练集来构建一个图像库，并为每个类别提取多个原型特征，以此捕捉类别内的异质性。设计输入特征与特定类别的原型之间的匹配损失，采用对比学习的方法，以解决类别间同质性问题并引导模型生成更准确的CAMs。 在四个数据集（LUAD-HistoSeg、BCSS-WSSS、GCSS 和 BCSS）上的实验表明，我们的方法超越了现有的弱监督分割方法，在组织病理学图像分割领域设立了新的性能基准。
URL
https://arxiv.org/abs/2503.12068
