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

Kupás, Dávid and Harangi, Balázs (2021) Solving the problem of imbalanced dataset with synthetic image generation for cell classification using deep learning. In: 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. (Unpublished)

Text EMBC_2021_final.pdf Restricted to Repository staff only Download (423kB)

Abstract

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.

Actions (login required)

Edit Item