HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Images

TL;DR

This paper introduces HistoSegCap, a novel capsule network-based weakly supervised segmentation method for histological tissue types in whole slide images, improving accuracy and efficiency in digital pathology analysis.

Contribution

It is the first application of Capsule Networks for weakly supervised semantic segmentation in histopathology, demonstrating superior performance over traditional methods.

Findings

Outperforms traditional segmentation methods in accuracy.

Achieves higher mean Intersection-over-Union (mIoU).

Enhances precision and efficiency in tissue analysis.

Abstract

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.