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Authors: Yuguang Li 1 ; Ezgi Mercan 1 ; Stevan Knezevitch 2 ; Joann G. Elmore 1 and Linda G. Shapiro 1

Affiliations: 1 University of Washington, United States ; 2 Pathology Associates, United States

ISBN: 978-989-758-276-9

Keyword(s): Breast Pathology, Automated Mitosis Detection, Convolutional Neural Networks, Histopathological Image Analysis.

Related Ontology Subjects/Areas/Topics: Applications ; Classification ; Medical Imaging ; Pattern Recognition ; Software Engineering ; Theory and Methods

Abstract: The analysis of breast cancer images includes the detection of mitotic figures whose counting is important in the grading of invasive breast cancer. Mitotic figures are difficult to find in the very large whole slide images, as they may look only slightly different from normal nuclei. In the last few years, several convolutional neural network (CNN) systems have been developed for mitosis detection that are able to beat conventional, feature-based approaches. However, these networks contain many layers and many neurons per layer, so both training and actual classification require powerful computers with GPUs. In this paper, we describe a new lightweight region-based CNN methodology we have developed that is able to run on standard machines with only a CPU and can achieve accuracy measures that are almost as good as the best CNN-based system so far in a fraction of the time, when both are run on CPUs. Our system, which includes a feature-based region extractor plus two CNN stages, is t ested on the ICPR 2012 and ICPR 2014 datasets, and results are given for accuracy and timing. (More)

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Paper citation in several formats:
Li, Y.; Mercan, E.; Knezevitch, S.; Elmore, J. and Shapiro, L. (2018). Efficient and Accurate Mitosis Detection - A Lightweight RCNN Approach.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 69-77. DOI: 10.5220/0006550700690077

@conference{icpram18,
author={Yuguang Li. and Ezgi Mercan. and Stevan Knezevitch. and Joann G. Elmore. and Linda G. Shapiro.},
title={Efficient and Accurate Mitosis Detection - A Lightweight RCNN Approach},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={69-77},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006550700690077},
isbn={978-989-758-276-9},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Efficient and Accurate Mitosis Detection - A Lightweight RCNN Approach
SN - 978-989-758-276-9
AU - Li, Y.
AU - Mercan, E.
AU - Knezevitch, S.
AU - Elmore, J.
AU - Shapiro, L.
PY - 2018
SP - 69
EP - 77
DO - 10.5220/0006550700690077

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