Endoscopic imaging is commonly used to diagnose Ulcerative Colitis (UC) and classify its severity. It has been shown that deep learning based methods are effective in automated analysis of these images and can potentially be used to aid medical doctors. Unleashing the full potential of these methods depends on the availability of large amount of labeled images; however, obtaining and labeling these images are quite challenging. In this paper, we propose a active learning based generative augmentation method. The method involves generating a large number of synthetic samples by training using a small dataset consisting of real endoscopic images. The resulting data pool is narrowed down by using active learning methods to select the most informative samples, which are then used to train a classifier. We demonstrate the effectiveness of our method through experiments on a publicly available endoscopic image dataset. The results show that using synthesized samples in conjunction with active learning leads to improved classification performance compared to using only the original labeled examples and the baseline classification performance of 68.1% increases to 74.5% in terms of Quadratic Weighted Kappa (QWK) Score. Another observation is that, attaining equivalent performance using only real data necessitated three times higher number of images.

翻译：内镜成像常用于诊断溃疡性结肠炎（UC）并对其严重程度进行分级。已有研究表明，基于深度学习的方法在自动分析这些图像方面效果显著，并可能用于辅助医生。充分发挥这些方法的潜力依赖于大量标注图像的可用性；然而，获取和标注这些图像相当具有挑战性。在本文中，我们提出了一种基于主动学习的生成式增强方法。该方法通过使用由少量真实内镜图像组成的小型数据集进行训练，生成大量合成样本。随后利用主动学习方法选择信息量最丰富的样本对生成的数据池进行缩减，并用于训练分类器。我们通过公开的内镜图像数据集上的实验证明了该方法的有效性。结果表明，与仅使用原始标注样本相比，将合成样本与主动学习相结合可提升分类性能，基线分类性能（Quadratic Weighted Kappa（QWK）评分）从68.1%提升至74.5%。另一发现是，仅使用真实数据达到同等性能所需图像数量需增加三倍。