# Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning

arXiv: https://arxiv.org/abs/2205.01684 · HTML: https://mlsanity.com/paper/2205.01684/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 2 of 2)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)

## Built on by (most cited first, 3 of 3)

- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification](https://mlsanity.com/paper/2401.03912/) (2024)
- [Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis](https://mlsanity.com/paper/2607.26043/) (2026)

## Abstract (arXiv)

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.