用于弱监督组织病理图像分割的基于原型的图像提示
Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation
AI总结：
针对组织病理图像弱监督分割中CAM不完整及类间同质、类内异质问题，提出基于原型的图像提示框架，利用聚类原型与对比匹配损失提升CAM质量，并在四个数据集上取得新基准。
摘要：
由于像素级标注成本高昂，使用图像级标签的弱监督图像分割已引起关注。使用类激活图（CAMs）的传统方法往往只突出最具判别性的区域，导致掩码不完整。近期引入文本信息的方法由于类间同质性和类内异质性，在处理组织病理图像时遇到困难。本文提出一种用于组织病理图像分割的、基于原型的图像提示框架。该框架利用聚类从训练集中构建图像库，为每个类别提取多个原型特征，以捕捉类内异质性。通过利用对比学习设计输入特征与类别特定原型之间的匹配损失，该方法解决了类间同质性问题，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，该方法优于现有弱监督分割方法，在组织病理图像分割方面建立了新的基准。
英文摘要：
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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