Detection of mitotic cells in histology images is an important but challenging process due to the resemblance of mitotic cells with other non-mitotic cells and also due to the different appearance of mitotic cells undergoing different phases of the division process. In this paper, we present an algorithm for classification of mitotic cells into its four different phases using eigenphase nuclei images — nuclear exemplars obtained separately from the eigen-decomposition of training nuclei images belonging to each of the four mitotic phases. To the best of our knowledge, ours is the first method to identify mitotic phases in cancer histology images. It is quite likely that the classification results may be negatively affected if the dataset used for training purposes does not contain sufficient number of samples for a positive class. To overcome this class imbalance problem, we present a novel method for oversampling the minority class. The proposed method generates synthetic images for training purposes by perturbing the representation of training samples belonging to the minority class in the eigenphase domain. We show that this strategy works effectively for pairwise classification of the mitotic cells — increasing the classification performance by as much as 24%.
['Ruqayya Awan', 'Nada Ashqar Aloraidi', 'Uvais Qidwai', 'Nasir M. Rajpoot']
How divided is a cell? Eigenphase nuclei for classification of mitotic phase in cancer histology images