Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning
This is an incremental improvement for breast cancer diagnosis workflows, potentially aiding radiologists in early detection.
The authors tackled the problem of classifying suspicious calcifications in mammograms by applying random histogram equalization as a data augmentation technique, resulting in improvements of over 1% in mean accuracy and F1-score on the CBIS-DDSM dataset.
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.