Efficient and Accurate Mitosis Detection - A Lightweight RCNN Approach

Yuguang Li, Ezgi Mercan, Stevan Knezevitch, Joann G. Elmore, Linda G. Shapiro

2018

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 tested on the ICPR 2012 and ICPR 2014 datasets, and results are given for accuracy and timing.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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