Deep detection and classification of mitotic figures

Breast cancer is the second largest cause of cancer death among women after skin cancer. Mitotic count is an important biomarker for predicting the breast cancer prognosis according to Nottingham Grading System. Pathologists look for tumour areas and select 10 HPF(high power field) images and assign a grade based on the number of mitotic counts. Mitosis detection is a tedious task because the pathologist has to inspect a larger area. The pathologist's views about mitotic cell are also subjective. Because of these problems, an assisting tool for the pathologist will generalize and reduce the time for diagnosis. Due to recent advancements in whole slide imaging, CAD(computer-aided diagnosis) systems are becoming popular. Mitosis detection for scanner images is difficult because of variability in shape, color, texture and its similar appearance to apoptotic nuclei, darkly stained nuclei structures. In this paper, the mitotic detection task is carried out with state of the art object detector (Faster R-CNN) and classifiers (Resnet152, Densenet169, and Densenet201) for ICPR 2012 dataset. The Faster R-CNN is used in two ways. In first, it was treated as an object detector which gave an F1-score of 0.79 while in second, it was treated as a Region Proposal Network followed by an ensemble of classifiers giving an F1-score 0.75.