论文标题
一种基于透明策略的新型数据增强方法，用于乳房X线照片分类
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms
论文作者
论文摘要
已经广泛研究了图像增强技术，以提高乳房X线摄影分类任务的深度学习（DL）算法的性能。最近的方法证明了图像增加对数据缺陷或数据不平衡问题的效率。在本文中，我们提出了一种新颖的透明度策略，以增强乳房X线照片分类器的乳房成像报告和数据系统（BI-RADS）评分。提出的方法利用感兴趣的区域（ROI）信息从原始图像中生成了更多的高风险训练示例（Bi-Rads 3、4、5）。我们在三个不同数据集上进行的广泛实验表明，所提出的方法显着改善了乳房X光分类的性能，并超过了一种称为CutMix的最先进的数据增强技术。这项研究还强调，我们的透明度方法比对BIADS分类的其他增强策略更有效，并且可以广泛应用于其他计算机视觉任务。
Image augmentation techniques have been widely investigated to improve the performance of deep learning (DL) algorithms on mammography classification tasks. Recent methods have proved the efficiency of image augmentation on data deficiency or data imbalance issues. In this paper, we propose a novel transparency strategy to boost the Breast Imaging Reporting and Data System (BI-RADS) scores of mammogram classifiers. The proposed approach utilizes the Region of Interest (ROI) information to generate more high-risk training examples for breast cancer (BI-RADS 3, 4, 5) from original images. Our extensive experiments on three different datasets show that the proposed approach significantly improves the mammogram classification performance and surpasses a state-of-the-art data augmentation technique called CutMix. This study also highlights that our transparency method is more effective than other augmentation strategies for BI-RADS classification and can be widely applied to other computer vision tasks.
