With the rapid advancement of deep learning, computational pathology has made significant progress in cancer diagnosis and subtyping. Tissue segmentation is a core challenge, essential for prognosis and treatment decisions. Weakly supervised semantic segmentation (WSSS) reduces the annotation requirement by using image-level labels instead of pixel-level ones. However, Class Activation Map (CAM)-based methods still suffer from low spatial resolution and unclear boundaries. To address these issues, we propose a multi-level superpixel correction algorithm that refines CAM boundaries using superpixel clustering and floodfill. Experimental results show that our method achieves great performance on breast cancer segmentation dataset with mIoU of 71.08%, significantly improving tumor microenvironment boundary delineation.

本研究解决了弱监督语义分割方法在组织病理图像中边界模糊和空间分辨率低的问题。提出了一种多层超像素修正算法，通过超像素聚类和填充技术来改善类激活图的边界定义。实验结果表明，该方法在乳腺癌分割数据集上取得了71.08%的mIoU，相较之前方法显著提升了肿瘤微环境的边界描绘。

弱监督语义分割中的超像素边界修正：以组织病理图像为例