Abstract：
Breast cancer is the most common cancer in women. Ultrasound is one of the most used techniques for diagnosis, but an expert in the field is necessary to interpret the test. Computer-aided diagnosis (CAD) systems aim to help physicians during this process. Experts use the Breast Imaging-Reporting and Data System (BI-RADS) to describe tumors according to several features (shape, margin, orientation...) and estimate their malignancy, with a common language. To aid in tumor diagnosis with BI-RADS explanations, this paper presents a deep neural network for tumor detection, description, and classification. An expert radiologist described with BI-RADS terms 749 nodules taken from public datasets. The YOLO detection algorithm is used to obtain Regions of Interest (ROIs), and then a model, based on a multi-class classification architecture, receives as input each ROI and outputs the BI-RADS descriptors, the BI-RADS classification (with 6 categories), and a Boolean classification of malignancy. Six hundred of the nodules were used for 10-fold cross-validation (CV) and 149 for testing. The accuracy of this model was compared with state-of-the-art CNNs for the same task. This model outperforms plain classifiers in the agreement with the expert (Cohen\'s kappa), with a mean over the descriptors of 0.58 in CV and 0.64 in testing, while the second best model yielded kappas of 0.55 and 0.59, respectively. Adding YOLO to the model significantly enhances the performance (0.16 in CV and 0.09 in testing). More importantly, training the model with BI-RADS descriptors enables the explainability of the Boolean malignancy classification without reducing accuracy.
摘要：
乳腺癌是女性最常见的癌症。超声是最常用的诊断技术之一，但是该领域的专家需要解释该测试。计算机辅助诊断（CAD）系统旨在在此过程中帮助医生。专家使用乳腺成像报告和数据系统(BI-RADS)根据几个特征(形状，margin,定位。..)并估计它们的恶性程度，用一种共同的语言。为了通过BI-RADS解释来帮助肿瘤诊断，本文提出了一种用于肿瘤检测的深度神经网络，描述,和分类。一位放射科专家用BI-RADS术语描述了从公共数据集中获取的749个结节。YOLO检测算法用于获得感兴趣区域(ROI)，然后是一个模型，基于多类分类架构，接收每个ROI作为输入，并输出BI-RADS描述符，BI-RADS分类(有6个类别)，和恶性肿瘤的布尔分类。600个结节用于10倍交叉验证（CV），149个用于测试。将该模型的准确性与同一任务的最新CNN进行了比较。该模型在与专家(科恩的kappa)的协议中优于普通分类器，在CV和测试中，描述符的平均值为0.58，在测试中为0.64，而第二好的模型产生的kappas分别为0.55和0.59。将YOLO添加到模型中可显著增强性能（在CV中为0.16，在测试中为0.09）。更重要的是,使用BI-RADS描述符训练模型可以在不降低准确性的情况下实现布尔恶性肿瘤分类的可解释性。
