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RCNN for region of interest detection in whole slide images

Nugaliyadde, A., Wong, K.W., Parry, J., Sohel, F., Laga, H.ORCID: 0000-0002-4758-7510, Somaratne, U.V., Yeomans, C. and Foster, O. (2020) RCNN for region of interest detection in whole slide images. In: Yang, H., Pasupa, K., Chi-Sing Leung, A., Kwok, J.T., Chan, J.H. and King, I., (eds.) Neural Information Processing. Springer, Cham, pp. 625-632.

Link to Published Version: https://doi.org/10.1007/978-3-030-63823-8_71
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Abstract

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.

Item Type: Book Chapter
Murdoch Affiliation(s): Information Technology, Mathematics and Statistics
Publisher: Springer, Cham
Copyright: © 2020 Springer Nature Switzerland AG
Other Information: Part of the Communications in Computer and Information Science book series (CCIS, volume 1333)
URI: http://researchrepository.murdoch.edu.au/id/eprint/59062
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