论文标题
使用深度学习的随机直方直方均衡对乳腺钙化分析的影响
Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning
论文作者
论文摘要
乳房X线照片图像中钙化的早期检测和分析对于乳腺癌诊断工作流程至关重要。需要立即进行随访并进一步分析其良性或恶性肿瘤的钙化可能会导致更好的预后。最近的研究表明，基于深度学习的算法可以学习强大的表示，以分析乳房X线摄影中的可疑钙化。在这项工作中，我们证明，将钙化斑块的直方图作为数据增强技术可以显着改善分析可疑钙化的分类性能。我们通过使用CBIS-DDSM数据集进行两个分类任务来验证我们的方法。这两个任务的结果都表明，与不使用直方图均衡相比，提出的方法在数据均衡时以0.4的概率均衡时获得了超过1％的平均准确性和F1得分。 t检验进一步支持了这一点，在该检验中，我们获得了p <0.0001的p值，从而显示了我们方法的统计学意义。
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.
