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Data imbalance remains one of the open challenges in the contemporary machinelearning. It is especially prevalent in case of medical data, such ashistopathological images. Traditional data-level approaches for dealing withdata imbalance are ill-suited for image data: oversampling methods such asSMOTE and its derivatives lead to creation of unrealistic syntheticobservations, whereas undersampling reduces the amount of available data,critical for successful training of convolutional neural networks. To alleviatethe problems associated with over- and undersampling we propose a noveltwo-stage resampling methodology, in which we initially use the oversamplingtechniques in the image space to leverage a large amount of data for trainingof a convolutional neural network, and afterwards apply undersampling in thefeature space to fine-tune the last layers of the network. Experimentsconducted on a colorectal cancer image dataset indicate the usefulness of theproposed approach.

Two-Stage Resampling for Convolutional Neural Network Training in the Imbalanced Colorectal Cancer Image Classification