  Early detection and analysis of calcifications in mammogram images is crucial
in a breast cancer diagnosis workflow. Management of calcifications that
require immediate follow-up and further analyzing its benignancy or malignancy
can result in a better prognosis. Recent studies have shown that deep
learning-based algorithms can learn robust representations to analyze
suspicious calcifications in mammography. In this work, we demonstrate that
randomly equalizing the histograms of calcification patches as a data
augmentation technique can significantly improve the classification performance
for analyzing suspicious calcifications. We validate our approach by using the
CBIS-DDSM dataset for two classification tasks. The results on both the tasks
show that the proposed methodology gains more than 1% mean accuracy and
F1-score when equalizing the data with a probability of 0.4 when compared to
not using histogram equalization. This is further supported by the t-tests,
where we obtain a p-value of p<0.0001, thus showing the statistical
significance of our approach.
