A Novel Cell Map Representation for Weakly Supervised Prediction of ER and PR Status from H&E WSIs
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%.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
