We propose a novel approach to breast mass classification based on deep learning models that utilize raw radio-frequency (RF) ultrasound (US) signals. US images, typically displayed by US scanners and used to develop computer-aided diagnosis systems, are reconstructed using raw RF data. However, information related to physical properties of tissues present in RF signals is partially lost due to the irreversible compression necessary to make raw data readable to the human eye. To utilize the information present in raw US data, we develop deep learning models that can automatically process small 2D patches of RF signals and their amplitude samples. We compare our approach with classification method based on the Nakagami parameter, a widely used quantitative US technique utilizing RF data amplitude samples. Our better performing deep learning model, trained using RF signals and their envelope samples, achieved good classification performance, with the area under the receiver attaining operating characteristic curve (AUC) and balanced accuracy of 0.772 and 0.710, respectively. The proposed method significantly outperformed the Nakagami parameter-based classifier, which achieved AUC and accuracy of 0.64 and 0.611, respectively. The developed deep learning models were used to generate parametric maps illustrating the level of mass malignancy. Our study presents the feasibility of using RF data for the development of deep learning breast mass classification models.

我们提出了一种基于深度学习模型的乳房质量分类的新方法，该模型利用原始射频（RF）超声（US）信号。使用原始RF数据重建通常由美国扫描仪显示并用于开发计算机辅助诊断系统的美国图像。然而，由于使原始数据对人眼可读所必需的不可逆压缩，与RF信号中存在的组织的物理特性有关的信息会部分丢失。为了利用美国原始数据中存在的信息，我们开发了深度学习模型，可以自动处理RF信号的小型2D斑块及其幅度样本。我们将我们的方法与基于Nakagami参数的分类方法进行了比较，Nakagami参数是一种广泛使用的利用RF数据幅度样本的定量美国技术。我们性能更好的深度学习模型，使用RF信号及其包络样本进行训练后，获得了良好的分类性能，接收机下方的区域分别达到了工作特性曲线（AUC）和0.772和0.710的平衡精度。该方法明显优于基于Nakagami参数的分类器，该分类器的AUC和准确度分别为0.64和0.611。开发的深度学习模型用于生成说明恶性肿瘤水平的参数图。我们的研究提出了使用RF数据开发深度学习乳房质量分类模型的可行性。该方法明显优于基于Nakagami参数的分类器，该分类器的AUC和准确度分别为0.64和0.611。开发的深度学习模型用于生成说明恶性肿瘤水平的参数图。我们的研究提出了使用RF数据开发深度学习乳房质量分类模型的可行性。该方法明显优于基于Nakagami参数的分类器，该分类器的AUC和准确度分别为0.64和0.611。开发的深度学习模型用于生成说明恶性肿瘤水平的参数图。我们的研究提出了使用RF数据开发深度学习乳房质量分类模型的可行性。