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

Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels