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Abstract：
Masses are one of the early signs of breast cancer, and the survival rate of women suffering from breast cancer can be improved if masses can be correctly identified as benign or malignant. However, their classification is challenging due to the similarity in texture patterns of both types of mass. The existing methods for this problem have low sensitivity and specificity. Based on the hypothesis that diverse contextual information of a mass region forms a strong indicator for discriminating benign and malignant masses and the idea of the ensemble classifier, we introduce a computer-aided system for this problem. The system uses multiple regions of interest (ROIs) encompassing a mass region for modeling diverse contextual information, a single ResNet-50 model (or its density-specific modification) as a backbone for local decisions, and stacking with SVM as a base model to predict the final decision. A data augmentation technique is introduced for fine-tuning the backbone model. The system was thoroughly evaluated on the benchmark CBIS-DDSM dataset using its provided data split protocol, and it achieved a sensitivity of 98.48% and a specificity of 92.31%. Furthermore, it was found that the system gives higher performance if it is trained and tested using the data from a specific breast density BI-RADS class. The system does not need to fine-tune/train multiple CNN models; it introduces diverse contextual information by multiple ROIs. The comparison shows that the method outperforms the state-of-the-art methods for classifying mass regions into benign and malignant. It will help radiologists reduce their burden and enhance their sensitivity in the prediction of malignant masses.
摘要：
肿块是乳腺癌的早期征兆之一，如果可以正确地将肿块识别为良性或恶性，则可以提高患有乳腺癌的妇女的生存率。然而,由于两种质量的纹理模式相似，它们的分类具有挑战性。针对该问题的现有方法具有低的灵敏度和特异性。基于以下假设：质量区域的不同上下文信息构成了区分良性和恶性质量的强大指标，以及集成分类器的思想，我们介绍了一个计算机辅助系统来解决这个问题。该系统使用多个感兴趣区域（ROI），其中包含大量区域，用于对各种上下文信息进行建模。单个ResNet-50模型（或其密度特定的修改）作为本地决策的骨干，并以SVM作为基础模型进行叠加，以预测最终决策。引入了一种数据增强技术来微调骨干模型。该系统使用其提供的数据拆分协议在基准CBIS-DDSM数据集上进行了彻底评估，灵敏度为98.48%,特异性为92.31%。此外，发现如果使用来自特定乳腺密度BI-RADS类的数据进行训练和测试,则该系统具有更高的性能。该系统不需要微调/训练多个CNN模型;它通过多个ROI引入不同的上下文信息。比较表明，该方法优于将肿块区域分类为良性和恶性的最新方法。这将有助于放射科医生减轻负担并提高其在恶性肿块预测中的敏感性。
