Early diagnosis and treatment of breast cancer can effectively reduce mortality. Since mammogram is one of the most commonly used methods in the early diagnosis of breast cancer, the classification of mammogram images is an important work of computer-aided diagnosis (CAD) systems. With the development of deep learning in CAD, deep convolutional neural networks have been shown to have the ability to complete the classification of breast cancer tumor patches with high quality, which makes most previous CNN-based full-field mammography classification methods rely on region of interest (ROI) or segmentation annotation to enable the model to locate and focus on small tumor regions. However, the dependence on ROI greatly limits the development of CAD, because obtaining a large number of reliable ROI annotations is expensive and difficult. Some full-field mammography image classification algorithms use multi-stage training or multi-feature extractors to get rid of the dependence on ROI, which increases the computational amount of the model and feature redundancy. In order to reduce the cost of model training and make full use of the feature extraction capability of CNN, we propose a deep multi-scale region selection network (MRSN) in deep features for end-to-end training to classify full-field mammography without ROI or segmentation annotation. Inspired by the idea of multi-example learning and the patch classifier, MRSN filters the feature information and saves only the feature information of the tumor region to make the performance of the full-field image classifier closer to the patch classifier. MRSN first scores different regions under different dimensions to obtain the location information of tumor regions. Then, a few high-scoring regions are selected by location information as feature representations of the entire image, allowing the model to focus on the tumor region. Experiments on two public datasets and one private dataset prove that the proposed MRSN achieves the most advanced performance.

中文翻译：

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用于全视野乳腺 X 线摄影分类的深度特征中的多尺度区域选择网络


乳腺癌的早期诊断和治疗可以有效降低死亡率。由于乳腺 X 光检查是乳腺癌早期诊断中最常用的方法之一，因此乳腺 X 光检查图像的分类是计算机辅助诊断 （CAD） 系统的一项重要工作。随着深度学习在 CAD 中的发展，深度卷积神经网络已被证明具有高质量完成乳腺癌肿瘤斑片分类的能力，这使得以前大多数基于 CNN 的全场乳腺 X 线摄影分类方法依赖于感兴趣区域 （ROI） 或分割注释，使模型能够定位和关注较小的肿瘤区域。然而，对 ROI 的依赖极大地限制了 CAD 的发展，因为获得大量可靠的 ROI 注释既昂贵又困难。一些全视野乳腺 X 线摄影图像分类算法使用多阶段训练或多特征提取器来摆脱对 ROI 的依赖，这增加了模型的计算量和特征冗余。为了降低模型训练成本并充分利用 CNN 的特征提取能力，我们提出了一种深度多尺度区域选择网络 （MRSN） 在深度特征中进行端到端训练，无需 ROI 或分割注释即可对全视野乳腺 X 线摄影进行分类。受多例学习和贴片分类器思想的启发，MRSN 过滤特征信息，只保存肿瘤区域的特征信息，使全视野图像分类器的性能更接近贴片分类器。MRSN 首先对不同维度下的不同区域进行评分，以获得肿瘤区域的位置信息。 然后，通过位置信息选择一些高分区域作为整个图像的特征表示，使模型能够专注于肿瘤区域。在两个公共数据集和一个私有数据集上的实验证明，所提出的 MRSN 实现了最先进的性能。