RCNN for Region of Interest Detection in Whole Slide Images
This work addresses the challenge of analyzing large-scale digital pathology images for pathologists, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of detecting Regions of Interest (ROIs) in Whole Slide Images for cancer detection by applying RCNN, a deep learning technique, using a small dataset of 60 WSIs for training and 12 for testing, and found it effective for ROI detection.
Digital pathology has attracted significant attention in recent years. Analysis of Whole Slide Images (WSIs) is challenging because they are very large, i.e., of Giga-pixel resolution. Identifying Regions of Interest (ROIs) is the first step for pathologists to analyse further the regions of diagnostic interest for cancer detection and other anomalies. In this paper, we investigate the use of RCNN, which is a deep machine learning technique, for detecting such ROIs only using a small number of labelled WSIs for training. For experimentation, we used real WSIs from a public hospital pathology service in Western Australia. We used 60 WSIs for training the RCNN model and another 12 WSIs for testing. The model was further tested on a new set of unseen WSIs. The results show that RCNN can be effectively used for ROI detection from WSIs.