Digital pathology involves converting physical tissue slides into high-resolution Whole Slide Images (WSIs), which pathologists analyze for disease-affected tissues. However, large histology slides with numerous microscopic fields pose challenges for visual search. To aid pathologists, Computer Aided Diagnosis (CAD) systems offer visual assistance in efficiently examining WSIs and identifying diagnostically relevant regions. This paper presents a novel histopathological image analysis method employing Weakly Supervised Semantic Segmentation (WSSS) based on Capsule Networks, the first such application. The proposed model is evaluated using the Atlas of Digital Pathology (ADP) dataset and its performance is compared with other histopathological semantic segmentation methodologies. The findings underscore the potential of Capsule Networks in enhancing the precision and efficiency of histopathological image analysis. Experimental results show that the proposed model outperforms traditional methods in terms of accuracy and the mean Intersection-over-Union (mIoU) metric.

本文提出了一种利用胶囊网络进行基于弱监督语义分割(WSSS)的新型组织病理学图像分析方法，并使用数字病理学图谱(ADP)数据集对其进行评估。实验结果表明，该模型在准确性和平均交并联度(mIoU)指标方面优于传统方法，凸显了胶囊网络在增强组织病理学图像分析的精度和效率方面的潜力。

HistoSegCap: 针对全切片图像中组织类型进行弱监督语义分割的胶囊