A Novel Cell Map Representation for Weakly Supervised Prediction of ER and PR Status from H&E WSIs

Hammam Alghamdi, Navid Alemi Koohbanani, Nasir Rajpoot, SHAN E AHMED RAZA

Keywords: Computational Pathology, ER/PR prediction, Compressed representations, Breast cancer

TL;DR: We propose a novel technique that creates a compressed representation of histology images, which is composed of cellular maps and compresses the WSIs while keeping relevant information at hand including the spatial relationships between cells.

Abstract: Digital pathology opens new pathways for computational algorithms to play a significant role in the prognosis, diagnosis, and analysis of cancer. However, handling large whole slide images (WSIs) is a vital challenge that these algorithms encounter. In this paper, we propose a novel technique that creates a compressed representation of histology images. This representation is composed of cellular maps and compresses the WSIs while keeping relevant information at hand including the spatial relationships between cells. The compression technique is used to predict the status of ER and PR expressions from H&E images. Our results show that the proposed compression technique can improve the prediction performance by 11-26%.