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.

翻译：在许多临床研究中,计算机辅助诊断用深层学习技术证明有助于诊断乳房X光造影学,然而,不同供应商的图像风格非常独特,不同供应商之间可能存在领域差距,有可能损害一个深层学习模式的普遍适用性。在本研究中,我们明确解决了拟议多分辨率和多参考神经风格传输(mr2NST)网络的风格多样性问题。Mr2NST可以将不同供应商的风格与同一风格基线的风格统一,并具有很高的分辨率。我们说明,所传送图像的图像质量与NIMA分数的目标域(供应商)原始图像的质量相当。同时,Dr2NST结果也证明有助于乳房造影中的损伤检测。