组织病理图像的语义分割对于自动癌症诊断非常重要，但其挑战在于获得用于训练的像素级标签需要耗费大量时间和人力。为了降低标注成本，弱监督语义分割（WSSS）旨在仅利用图像或补丁级别分类标签来分割对象。目前的WSSS方法大多基于通常定位出具有限分割准确性的最具有区分性的对象部分的类活动图（CAM）。本研究提出了一种基于高分辨率激活图和交替学习（HAMIL）的新型两阶段弱监督分割框架。首先，提出了一个简单而有效的具有高分辨率激活图（HAM-Net）的分类网络，它利用轻量级分类头与多层融合（MLF）的激活图和蒙特卡罗增强（MCA）来获得精确的前景区域。其次，使用由HAM-Net生成的密集伪标签来训练更好的分割模型，其中使用相同结构的三个网络进行交替学习：两个网络之间的一致性用于突出可靠的伪标签以训练第三个网络，同时，两个网络作为教师通过知识蒸馏来指导第三个网络。在两个公共肺癌组织病理图像数据集上的大量实验表明，我们提出的HAMIL分别优于最先进的弱监督和噪声标签学习方法。代码可在https://github.com/HiLab-git/HAMIL上获得。

Semantic segmentation of histopathological images is important for automatic cancer diagnosis, and it is challenged by time-consuming and labor-intensive annotation process that obtains pixel-level labels for training. To reduce annotation costs, Weakly Supervised Semantic Segmentation (WSSS) aims to segment objects by only using image or patch-level classification labels. Current WSSS methods are mostly based on Class Activation Map (CAM) that usually locates the most discriminative object part with limited segmentation accuracy. In this work, we propose a novel two-stage weakly supervised segmentation framework based on High-resolution Activation Maps and Interleaved Learning (HAMIL). First, we propose a simple yet effective Classification Network with High-resolution Activation Maps (HAM-Net) that exploits a lightweight classification head combined with Multiple Layer Fusion (MLF) of activation maps and Monte Carlo Augmentation (MCA) to obtain precise foreground regions. Second, we use dense pseudo labels generated by HAM-Net to train a better segmentation model, where three networks with the same structure are trained with interleaved learning: The agreement between two networks is used to highlight reliable pseudo labels for training the third network, and at the same time, the two networks serve as teachers for guiding the third network via knowledge distillation. Extensive experiments on two public histopathological image datasets of lung cancer demonstrated that our proposed HAMIL outperformed state-of-the-art weakly supervised and noisy label learning methods, respectively. The code is available at https://github.com/HiLab-git/HAMIL.