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.
A Nugaliyadde, Kok Wai Wong, Jeremy Parry, Ferdous Sohel, Hamid Laga, Upeka V. Somaratne, Chris Yeomans, Orchid Foster