  Data imbalance remains one of the open challenges in the contemporary machine
learning. It is especially prevalent in case of medical data, such as
histopathological images. Traditional data-level approaches for dealing with
data imbalance are ill-suited for image data: oversampling methods such as
SMOTE and its derivatives lead to creation of unrealistic synthetic
observations, whereas undersampling reduces the amount of available data,
critical for successful training of convolutional neural networks. To alleviate
the problems associated with over- and undersampling we propose a novel
two-stage resampling methodology, in which we initially use the oversampling
techniques in the image space to leverage a large amount of data for training
of a convolutional neural network, and afterwards apply undersampling in the
feature space to fine-tune the last layers of the network. Experiments
conducted on a colorectal cancer image dataset indicate the usefulness of the
proposed approach.
