论文标题
用深层神经网络减少假阳性活检，这些活检利用局部和全球信息筛选乳房X线照片
Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
论文作者
论文摘要
乳腺癌是女性中最常见的癌症，数十万不必要的活检是在世界范围内进行的。降低原来是良性组织的活检率至关重要。在这项研究中，我们建立了深层神经网络（DNN），以将活检病变分类为恶性或良性，目的是将这些网络用作为放射线医生服务的第二读者，以进一步减少误报发现的数量。我们通过以从整个图像中学到的显着性图的形式整合提供的全球环境来提高了受过训练的DNN的性能，这些培训是从小图像贴片中学习的，类似于放射科医生在评估感兴趣领域时如何考虑全球环境的方式。我们的实验是在141,473例患者的229,426次筛查乳房X线摄影检查的数据集上进行的。我们在由464个良性和136个恶性病变组成的测试集上达到0.8。
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign, with the goal of using these networks as second readers serving radiologists to further reduce the number of false positive findings. We enhance the performance of DNNs that are trained to learn from small image patches by integrating global context provided in the form of saliency maps learned from the entire image into their reasoning, similar to how radiologists consider global context when evaluating areas of interest. Our experiments are conducted on a dataset of 229,426 screening mammography exams from 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464 benign and 136 malignant lesions.
