论文标题
关节2d-3d乳腺癌分类
Joint 2D-3D Breast Cancer Classification
论文作者
论文摘要
乳腺癌是导致女性癌症死亡人数最多的恶性肿瘤。数字乳房X线照片（DM或2D乳房X线照片）和数字乳房合成（DBT或3D乳房X线照片）是乳腺癌检测和诊断临床实践中使用的两种类型的乳房X线摄影图像。放射科医生通常会读这两种成像方式。但是，现有的计算机辅助诊断工具仅使用一种成像方式设计。受临床实践的启发，我们提出了一个创新的卷积神经网络（CNN）进行乳腺癌分类，同时使用2D和3D乳房X线照片。我们的实验表明，所提出的方法显着改善了乳腺癌分类的性能。通过组装三个CNN分类器，提出的模型可实现0.97 AUC，比仅使用一种成像模式的方法高34.72％。
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously. Our experiment shows that the proposed method significantly improves the performance of breast cancer classification. By assembling three CNN classifiers, the proposed model achieves 0.97 AUC, which is 34.72% higher than the methods using only one imaging modality.
