Harnessing generative adversarial networks to generate synthetic mitosis images for classification of cell images

The task of detecting and tracking of mitosis is important in many biomedical areas such as cancer and stem cell research. This task becomes complex when done in a high-density cell array, largely due to an extremely imbalanced data, with a very small number of proliferating cells in each image. Using the fact that before proliferating, cells seems to get rounder and brighter, our group extracted bright blobs in each image and considered the patch around each blob as a candidate for mitosis. These candidates were labeled and divided into training, validation and test sets, and used for training of a Convolutional Neural Network (CNN). In the current work, in order to overcome the small number of mitosis samples in the training set, we generated synthetic patches of mitosis using Generative Adversarial Networks (GANs). Trying to predict the labels of the test set candidates using a CNN trained by both real and the synthetically generated images showed an increase in both sensitivity and specificity, in comparison to a CNN trained only on real examples.