The performance of a CNN based medical-image classification network depends on the intensities of the trained images. Therefore, it is necessary to generalize medical images of various intensities against degradation of performance. For lesion classification, features of generalized images should be carefully maintained. To maintain the performance of the medical image classification network and minimize the loss of features, we propose a method using a generative adversarial network (GAN) as a generator to adapt the arbitrary intensity distribution to the specific intensity distribution of the training set. We also select CycleGAN and UNIT to train unpaired medical image data sets. The following was done to evaluate each method’s performance: the similarities between the generalized image and the original were measured via the structural similarity index (SSIM) and histogram, and the original domain data set was passed to a classifier that trained only the original domain images for accuracy comparisons. The results show that the performance evaluation of the generalized images is better than that of the originals, confirming that our proposed method is a simple but powerful solution to the performance degradation of a classification network.

中文翻译：

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使用GAN概括医学图像的强度分布

基于CNN的医学图像分类网络的性能取决于训练后图像的强度。因此，有必要对各种强度的医学图像进行概括以防止性能下降。对于病变分类，应仔细维护广义图像的特征。为了保持医学图像分类网络的性能并使特征损失最小化，我们提出了一种使用生成对抗网络（GAN）作为生成器的方法，以使任意强度分布适应训练集的特定强度分布。我们还选择CycleGAN和UNIT来训练未配对的医学图像数据集。进行以下操作以评估每种方法的性能：通过结构相似性指数（SSIM）和直方图测量广义图像与原始图像之间的相似性，并将原始域数据集传递给仅训练原始域图像以进行准确性比较的分类器。结果表明，广义图像的性能评估要优于原始图像，这证明了我们提出的方法是一种简单但强大的解决方案，可以有效地降低分类网络的性能。