Efficient and Accurate Mitosis Detection - A Lightweight RCNN Approach

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, featurebased 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