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Imbalanced datasets in medical imaging are characterized by skewed classproportions and scarcity of abnormal cases. When trained using such data,models tend to assign higher probabilities to normal cases, leading to biasedperformance. Common oversampling techniques such as SMOTE rely on localinformation and can introduce marginalization issues. This paper investigatesthe potential of using Mixup augmentation that combines two training examplesalong with their corresponding labels to generate new data points as a genericvicinal distribution. To this end, we propose STEM, which combines SMOTE-ENNand Mixup at the instance level. This integration enables us to effectivelyleverage the entire distribution of minority classes, thereby mitigating bothbetween-class and within-class imbalances. We focus on the breast cancerproblem, where imbalanced datasets are prevalent. The results demonstrate theeffectiveness of STEM, which achieves AUC values of 0.96 and 0.99 in theDigital Database for Screening Mammography and Wisconsin Breast Cancer(Diagnostics) datasets, respectively. Moreover, this method shows promisingpotential when applied with an ensemble of machine learning (ML) classifiers.

STEM Rebalance: A Novel Approach for Tackling Imbalanced Datasets using SMOTE, Edited Nearest Neighbour, and Mixup