HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of
  Histological Tissue Type in Whole Slide Images
Mobina Mansoori Sajjad Shahabodini Jamshid Abouei Arash Mohammadi Konstantinos N. Plataniotis
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.