论文标题
MR2NST：乳房X线摄影的多分辨率和多参考神经风格转移
mr2NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography
论文作者
论文摘要
在许多临床研究中，使用深度学习技术的计算机辅助诊断有助于诊断乳房X线摄影。但是，不同供应商的图像样式非常独特，并且不同供应商之间可能存在域间隙，这可能会损害一个深度学习模型的普遍适用性。在这项研究中，我们通过拟议的多分辨率和多参考神经风格转移（MR2NST）网络明确解决样式品种问题。 MR2NST可以将不同供应商的样式归一化，并具有很高的分辨率。我们说明，传输图像的图像质量与NIMA分数的原始图像（供应商）的原始图像质量相媲美。同时，MR2NST结果也证明有助于乳房X线照片中的病变检测。
Computer-aided diagnosis with deep learning techniques has been shown to be helpful for the diagnosis of the mammography in many clinical studies. However, the image styles of different vendors are very distinctive, and there may exist domain gap among different vendors that could potentially compromise the universal applicability of one deep learning model. In this study, we explicitly address style variety issue with the proposed multi-resolution and multi-reference neural style transfer (mr2NST) network. The mr2NST can normalize the styles from different vendors to the same style baseline with very high resolution. We illustrate that the image quality of the transferred images is comparable to the quality of original images of the target domain (vendor) in terms of NIMA scores. Meanwhile, the mr2NST results are also shown to be helpful for the lesion detection in mammograms.
