Disease severity regression by a convolutional neural network (CNN) for medical images requires a sufficient number of image samples labeled with severity levels. Conditional generative adversarial network (cGAN)-based data augmentation (DA) is a possible solution, but it encounters two issues. The first issue is that existing cGANs cannot deal with real-valued severity levels as their conditions, and the second is that the severity of the generated images is not fully reliable. We propose continuous DA as a solution to the two issues. Our method uses continuous severity GAN to generate images at real-valued severity levels and dataset-disjoint multi-objective optimization to deal with the second issue. Our method was evaluated for estimating ulcerative colitis (UC) severity of endoscopic images and achieved higher classification performance than conventional DA methods.

翻译：基于卷积神经网络（CNN）的医学图像疾病严重程度回归需要足够的带有严重程度标签的图像样本。基于条件生成对抗网络（cGAN）的数据增强是一种可能的解决方案，但存在两个问题：现有cGAN无法将以实数值表示的严重程度作为其条件，且生成图像的严重程度不完全可靠。我们提出连续数据增强来解决这两个问题。该方法使用连续严重程度生成对抗网络（连续严重程度GAN）生成具有实数值严重程度的图像，并通过数据集分离的多目标优化处理第二个问题。该方法被用于评估内窥镜图像中的溃疡性结肠炎（UC）严重程度，相较于传统数据增强方法取得了更高的分类性能。