论文标题
3D数字乳房合成分类的2D卷积神经网络
2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification
论文作者
论文摘要
乳腺癌检测的自动化方法集中在2D乳房X线摄影上，并且在很大程度上忽略了3D数字乳腺政策合成（DBT），该乳腺X线合成（DBT）经常用于临床实践。开发用于DBT分类的自动化方法的两个关键挑战正在处理可变数量的切片数并保留切片到拼上的更改。我们提出了一个新型的DEV 2D卷积神经网络（CNN）进行DBT分类，同时克服了这两个挑战。我们的方法在整个卷上运行，无论切片的数量多少，并允许使用预训练的2D CNN进行特征提取，这很重要，考虑到有限的注释培训数据。在对现实世界临床数据集的广泛评估中，我们的方法达到了0.854 AUROC，比基于3D CNN的方法高28.80％。我们还发现，这些改进在一系列模型配置中都是稳定的。
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice. The two key challenges in developing automated methods for DBT classification are handling the variable number of slices and retaining slice-to-slice changes. We propose a novel deep 2D convolutional neural network (CNN) architecture for DBT classification that simultaneously overcomes both challenges. Our approach operates on the full volume, regardless of the number of slices, and allows the use of pre-trained 2D CNNs for feature extraction, which is important given the limited amount of annotated training data. In an extensive evaluation on a real-world clinical dataset, our approach achieves 0.854 auROC, which is 28.80% higher than approaches based on 3D CNNs. We also find that these improvements are stable across a range of model configurations.
