Mednikov Y, Nehemia S, Zheng B, Benzaquen O, Lederman D
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2587-2590. doi: 10.1109/EMBC.2018.8512750.
Breast cancer is the most prevalent cancer among women. The most common method to detect breast cancer is mammography. However, interpreting mammography is a challenging task that requires high skills and is timeconsuming. In this work, we propose a computer-aided diagnosis (CAD) scheme for mammography based on transfer representation learning using the Inception-V3 architecture. We evaluate the performance of the proposed scheme using the INBreast database, where the features are extracted from different layers of the architecture. In order to cope with the small dataset size limitation, we expand the training dataset by generating artificial mammograms and employing different augmentation techniques. The proposed scheme shows great potential with a maximal area under the receiver operating characteristics curve of 0.91.
乳腺癌是女性中最常见的癌症。检测乳腺癌最常用的方法是乳房X光检查。然而，解读乳房X光检查结果是一项具有挑战性的任务，需要高技能且耗时。在这项工作中，我们提出了一种基于使用Inception-V3架构的迁移表示学习的乳房X光检查计算机辅助诊断（CAD）方案。我们使用INBreast数据库评估所提出方案的性能，其中特征是从该架构的不同层提取的。为了应对数据集规模小的限制，我们通过生成人工乳房X光图像并采用不同的增强技术来扩展训练数据集。所提出的方案显示出巨大潜力，在接收器操作特性曲线下的最大面积为0.91。
