  Breast cancer is among the most deadly diseases, distressing mostly women
worldwide. Although traditional methods for detection have presented themselves
as valid for the task, they still commonly present low accuracies and demand
considerable time and effort from professionals. Therefore, a computer-aided
diagnosis (CAD) system capable of providing early detection becomes hugely
desirable. In the last decade, machine learning-based techniques have been of
paramount importance in this context, since they are capable of extracting
essential information from data and reasoning about it. However, such
approaches still suffer from imbalanced data, specifically on medical issues,
where the number of healthy people samples is, in general, considerably higher
than the number of patients. Therefore this paper proposes the $\text{O}^2$PF,
a data oversampling method based on the unsupervised Optimum-Path Forest
Algorithm. Experiments conducted over the full oversampling scenario state the
robustness of the model, which is compared against three well-established
oversampling methods considering three breast cancer and three general-purpose
tasks for medical issues datasets.
