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Studies estimate that there will be 266,120 new cases of invasive breastcancer and 40,920 breast cancer induced deaths in the year of 2018 alone.Despite the pervasiveness of this affliction, the current process to obtain anaccurate breast cancer prognosis is tedious and time consuming, requiring atrained pathologist to manually examine histopathological images in order toidentify the features that characterize various cancer severity levels. Wepropose MITOS-RCNN: a novel region based convolutional neural network (RCNN)geared for small object detection to accurately grade one of the three factorsthat characterize tumor belligerence described by the Nottingham GradingSystem: mitotic count. Other computational approaches to mitotic figurecounting and detection do not demonstrate ample recall or precision to beclinically viable. Our models outperformed all previous participants in theICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challengealong with recently published works. Our model achieved an F-measure score of0.955, a 6.11% improvement in accuracy from the most accurate of the previouslyproposed models.

MITOS-RCNN: A Novel Approach to Mitotic Figure Detection in Breast Cancer Histopathology Images using Region Based Convolutional Neural Networks