  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.
