Early detection and treatment of breast cancer can significantly reduce patient mortality, and mammogram is an effective method for early screening. Computer-aided diagnosis (CAD) of mammography based on deep learning can assist radiologists in making more objective and accurate judgments. However, existing methods often depend on datasets with manual segmentation annotations. In addition, due to the large image sizes and small lesion proportions, many methods that do not use region of interest (ROI) mostly rely on multi-scale and multi-feature fusion models. These shortcomings increase the labor, money, and computational overhead of applying the model. Therefore, a deep location soft-embedding-based network with regional scoring (DLSEN-RS) is proposed. DLSEN-RS is an end-to-end mammography image classification method containing only one feature extractor and relies on positional embedding (PE) and aggregation pooling (AP) modules to locate lesion areas without bounding boxes, transfer learning, or multi-stage training. In particular, the introduced PE and AP modules exhibit versatility across various CNN models and improve the model’s tumor localization and diagnostic accuracy for mammography images. Experiments are conducted on published INbreast and CBIS-DDSM datasets, and compared to previous state-of-the-art mammographic image classification methods, DLSEN-RS performed satisfactorily.

中文翻译：

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基于深度位置软嵌入的网络，具有用于乳腺 X 线摄影分类的区域评分


乳腺癌的早期发现和治疗可以显著降低患者死亡率，乳房 X 光检查是早期筛查的有效方法。基于深度学习的乳腺 X 线摄影计算机辅助诊断 （CAD） 可以帮助放射科医生做出更客观、更准确的判断。但是，现有方法通常依赖于具有手动分割注释的数据集。此外，由于图像尺寸大、病灶比例小，许多不使用感兴趣区域 （ROI） 的方法大多依赖于多尺度和多特征融合模型。这些缺点增加了应用模型的人力、金钱和计算开销。因此，该文提出一种具有区域评分的基于深度位置软嵌入的网络（DLSEN-RS）。DLSEN-RS 是一种端到端乳腺 X 线摄影图像分类方法，仅包含一个特征提取器，并依靠位置嵌入 （PE） 和聚合池 （AP） 模块来定位病变区域，而无需边界框、迁移学习或多阶段训练。特别是，引入的 PE 和 AP 模块在各种 CNN 模型中表现出多功能性，并提高了模型的肿瘤定位和乳腺 X 线摄影图像的诊断准确性。在已发布的 INbreast 和 CBIS-DDSM 数据集上进行了实验，与以前最先进的乳腺 X 线图像分类方法相比，DLSEN-RS 的表现令人满意。