Machine learning has been widely used in medical image analysis under an assumption that the training and test data are under the same feature distributions. However, medical images from difference devices or the same device with different parameter settings are often contaminated with different amount and types of noises, which violate the above assumption. Therefore, the models trained using data from one device or setting often fail to work for that from another. Moreover, it is very expensive and tedious to label data and re-train models for all different devices or settings. To overcome this noise adaptation issue, it is necessary to leverage on the models trained with data from one device or setting for new data. In this paper, we reformulate this noise adaptation task as an image-to-image translation task such that the noise patterns from the test data are modified to be similar to those from the training data while the contents of the data are unchanged. In this paper, we propose a novel Noise Adaptation Generative Adversarial Network (NAGAN), which contains a generator and two discriminators. The generator aims to map the data from source domain to target domain. Among the two discriminators, one discriminator enforces the generated images to have the same noise patterns as those from the target domain, and the second discriminator enforces the content to be preserved in the generated images. We apply the proposed NAGAN on both optical coherence tomography (OCT) images and ultrasound images. Results show that the method is able to translate the noise style. In addition, we also evaluate our proposed method with segmentation task in OCT and classification task in ultrasound. The experimental results show that the proposed NAGAN improves the analysis outcome.

中文翻译：

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用于医学图像分析的噪声自适应生成对抗网络。

在训练和测试数据处于相同特征分布的假设下，机器学习已广泛用于医学图像分析。然而，来自不同设备或具有不同参数设置的同一设备的医学图像经常被不同数量和类型的噪声污染，这违反了上述假设。因此，使用来自一个设备或设置的数据训练的模型通常无法用于另一个设备或设置的数据。此外，为所有不同的设备或设置标记数据并重新训练模型非常昂贵且繁琐。为了克服这种噪声适应问题，有必要利用通过来自一个设备的数据或设置新数据训练的模型。在本文中，我们将这种噪声适应任务重新构造为图像到图像的转换任务，以便将测试数据中的噪声模式修改为与训练数据中的噪声模式相似，而数据内容不变。在本文中，我们提出了一个新颖的噪声自适应生成对抗网络（NAGAN），其中包含一个生成器和两个鉴别器。生成器旨在将数据从源域映射到目标域。在这两个鉴别器中，一个鉴别器强制生成的图像具有与目标域相同的噪声模式，第二个鉴别器强制将要保留的内容保留在生成的图像中。我们将建议的NAGAN应用于光学相干断层扫描（OCT）图像和超声图像。结果表明，该方法能够转化噪声样式。此外，我们还用OCT中的分割任务和超声中的分类任务来评估我们提出的方法。实验结果表明，提出的NAGAN可以改善分析结果。