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

TL;DR

This paper introduces a semi-supervised learning approach for ulcerative colitis classification that leverages location and image order features to improve accuracy with limited labeled data.

Contribution

It proposes a novel order-guided disentangled representation learning method that effectively utilizes auxiliary features for UC classification with few labels.

Findings

Outperforms existing semi-supervised methods in accuracy

Effective with limited annotated images

Utilizes location and order features for disentanglement

Abstract

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.