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Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning
Image and Video Processing · Submitted 3 May 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Early detection and analysis of calcifications in mammogram images is crucial in a breast cancer diagnosis workflow. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Management of calcifications that require immediate follow-up and further analyzing its benignancy or malignancy can result in a better prognosis. | background 0.99 | 0.05 | 0.02 | hedged possibility 0.63 |
| s2 | Recent studies have shown that deep learning-based algorithms can learn robust representations to analyze suspicious calcifications in mammography. | background 1.00 | 0.03 | 0.02 | asserted 0.99 |
| s3 | 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. | contribution 0.95 | 0.93 | 0.92 | asserted 1.00 |
| s4 | We validate our approach by using the CBIS-DDSM dataset for two classification tasks. | method 0.99 | 0.92 | 0.03 | asserted 0.98 |
| s5 | 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. | result 1.00 | 0.97 | 0.35 | asserted 1.00 |
| s6 | 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. | result 0.61 | 0.96 | 0.11 | asserted 1.00 |
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The paper
Original abstract
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.
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