Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis, involves two main difficulties. First, endoscopic images with the annotation about UC (positive or negative) are usually limited. Second, they show a large variability in their appearance due to the location in the colon. Especially, the second difficulty prevents us from using existing semi-supervised learning techniques, which are the common remedy for the first difficulty. In this paper, we propose a practical semi-supervised learning method for UC classification by newly exploiting two additional features, the location in a colon (e.g., left colon) and image capturing order, both of which are often attached to individual images in endoscopic image sequences. The proposed method can extract the essential information of UC classification efficiently by a disentanglement process with those features. Experimental results demonstrate that the proposed method outperforms several existing semi-supervised learning methods in the classification task, even with a small number of annotated images.

翻译：溃疡性结肠炎分类是内镜诊断的重要任务，面临两大挑战。首先，带有UC标注（阳性或阴性）的内镜图像通常数量有限；其次，由于结肠部位差异，图像外观存在显著变异。其中，第二个挑战尤其阻碍了现有半监督学习技术的应用（而该技术本是应对第一个挑战的常规方法）。本文提出一种实用的半监督学习方法，通过新近利用两个附加特征——结肠位置（如左结肠）和图像拍摄次序（内镜图像序列中单张图像常附带的属性），实现UC分类。所提方法能通过基于这些特征的解耦过程，高效提取UC分类的实质信息。实验结果表明，即使标注图像数量极少，该方法在分类任务中仍优于现有多种半监督学习方法。