Solving the problem of imbalanced dataset with synthetic image generation for cell classification using deep learning

The low number of annotated training images and class imbalance in the field of machine learning is a common problem that is faced in many applications. With this paper, we focus on a clinical dataset where cells were extracted in a previous research. Class imbalance can be experienced within this dataset since the normal cells are in a great majority in contrast to the abnormal ones. To address both problems, we present our idea of synthetic image generation using a custom variational autoencoder, that also enables the pretraining of the subsequent classifier network. Our method is compared with a performant solution, as well as presented with different modifications. We have experienced a performance increase of 4.52% regarding the classification of the abnormal cells.Clinical Relevance — We extract images from cervical smears, using digitized Pap test. When working with these kinds of smears, a single one can contain more than 10,000 cells. Examination of these is done manually by going over each cell individually. Our main goal is to make a system that can rank these samples by importance, thus making the process easier and more effective. The research that is described in this paper gets us a step closer to achieving our goal.