  Imbalanced datasets in medical imaging are characterized by skewed class
proportions and scarcity of abnormal cases. When trained using such data,
models tend to assign higher probabilities to normal cases, leading to biased
performance. Common oversampling techniques such as SMOTE rely on local
information and can introduce marginalization issues. This paper investigates
the potential of using Mixup augmentation that combines two training examples
along with their corresponding labels to generate new data points as a generic
vicinal distribution. To this end, we propose STEM, which combines SMOTE-ENN
and Mixup at the instance level. This integration enables us to effectively
leverage the entire distribution of minority classes, thereby mitigating both
between-class and within-class imbalances. We focus on the breast cancer
problem, where imbalanced datasets are prevalent. The results demonstrate the
effectiveness of STEM, which achieves AUC values of 0.96 and 0.99 in the
Digital Database for Screening Mammography and Wisconsin Breast Cancer
(Diagnostics) datasets, respectively. Moreover, this method shows promising
potential when applied with an ensemble of machine learning (ML) classifiers.
