IEEE Trans Med Imaging. 2020 Apr;39(4):1149-1159. doi: 10.1109/TMI.2019.2944488. Epub 2019 Sep 30.
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.
机器学习已广泛应用于医学图像分析中，其前提是训练数据和测试数据具有相同的特征分布。然而，来自不同设备或同一设备但参数设置不同的医学图像通常会受到不同数量和类型的噪声的污染，这违反了上述假设。因此，使用来自一种设备或设置的数据训练的模型通常无法在另一种设备或设置上使用。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。为了克服这个噪声适应问题，有必要利用从一种设备或设置训练的数据来处理新数据。在本文中，我们将这种噪声适应任务重新表述为图像到图像的翻译任务，使得测试数据中的噪声模式被修改为与训练数据中的噪声模式相似，而数据的内容保持不变。在本文中，我们提出了一种新颖的噪声适应生成对抗网络（NAGAN），它包含一个生成器和两个鉴别器。生成器旨在将源域的数据映射到目标域。在两个鉴别器中，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，第二个鉴别器强制生成的图像保留内容。我们将提出的 NAGAN 应用于光学相干断层扫描（OCT）图像和超声图像。结果表明，该方法能够转换噪声样式。此外，我们还在 OCT 的分割任务和超声的分类任务中评估了我们提出的方法。实验结果表明，所提出的 NAGAN 提高了分析结果。
