• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Workshop on Breast Imaging >Quality analysis of DCGAN-generated mammography lesions
【24h】

Quality analysis of DCGAN-generated mammography lesions

机译：DCGAN乳腺摄影病变的质量分析

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Medical image synthesis has gained a great focus recently, especially after the introduction of Generative Adversarial Networks (GANs). GANs have been used widely to provide anatomically-plausible and diverse samples for augmentation and other applications, including segmentation and super resolution. In our previous work, Deep Convolutional GANs were used to generate synthetic mammogram lesions, masses mainly, that could enhance the classification performance in imbalanced datasets. In this new work, a deeper investigation was carried out to explore other aspects of the generated images evaluation, i.e., realism, feature space distribution, and observer studies. t-Stochastic Neighbor Embedding (t-SNE) was used to reduce the dimensionality of real and fake images to enable 2D visualisations. Additionally, two expert radiologists performed a realism-evaluation study. Visualisations showed that the generated images have a similar feature distribution of the real ones, avoiding outliers. Moreover, the Receiver Operating Characteristic (ROC) study showed that the radiologists could not, in many cases, distinguish between synthetic and real lesions, giving accuracies between 51% and 59% using a balanced sample set.
机译：最近，医学图像合成受到了广泛关注，特别是在引入了对抗性生成网络（GANs）之后。 GAN已被广泛用于为扩增和其他应用（包括分割和超分辨率）提供解剖学上合理且多样化的样本。在我们之前的工作中，使用深度卷积GAN来生成合成乳房X线照片病变（主要是肿块），这可以增强不平衡数据集中的分类性能。在这项新工作中，我们进行了更深入的研究，以探索生成图像评估的其他方面，即现实性，特征空间分布和观察者研究。使用t随机邻居嵌入（t-SNE）来减少真实图像和伪图像的维数，以实现2D可视化。此外，两名放射线专家进行了真实性评估研究。可视化显示生成的图像具有与真实图像相似的特征分布，避免了离群值。此外，接收器工作特性（ROC）研究表明，放射科医生在许多情况下无法区分合成病变和真实病变，使用平衡的样本集可以得出51％至59％的准确度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号