HistoSegCap：用于全幻灯片图像中组织学组织类型弱监督语义分割的胶囊网络
HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Images
AI总结：
本文提出基于胶囊网络的弱监督语义分割方法HistoSegCap，用于全幻灯片图像中组织学组织类型分析，在ADP数据集上的准确性和mIoU指标优于传统方法。
摘要：
数字病理学涉及将物理组织切片转换为高分辨率的全幻灯片图像（WSIs），病理学家通过分析这些图像来寻找受疾病影响的组织。然而，包含大量显微视野的大型组织学切片给视觉搜索带来了挑战。为了辅助病理学家，计算机辅助诊断（CAD）系统提供视觉辅助，以高效检查WSIs并识别具有诊断相关性的区域。本文提出了一种新的组织病理学图像分析方法，采用基于胶囊网络的弱监督语义分割（WSSS），这是该方法的首次此类应用。所提出的模型使用数字病理学图谱（ADP）数据集进行评估，并将其性能与其他组织病理学语义分割方法进行了比较。研究结果强调了胶囊网络在提高组织病理学图像分析的精度和效率方面的潜力。实验结果表明，所提出的模型在准确性和平均交并比指标上优于传统方法。
英文摘要：
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.
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