Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels
Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels
复制标题
用于具有有限标签的溃疡性结肠炎分类的顺序引导解缠表示学习
DOI：
10.1007/978-3-030-87196-3_44
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Uchida Seiichi
中科院分区：
文献类型：
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作者：
Harada Shota;Bise Ryoma;Hayashi Hideaki;Tanaka Kiyohito;Uchida Seiichi
溃疡性结肠炎（UC）的分类是内镜诊断的重要任务，涉及两个主要困难。首先，具有关于UC（阳性或阴性）的注释的内窥镜图像通常是有限的。其次，由于在结肠中的位置，它们在外观上显示出很大的变化。特别是，第二个困难使我们无法使用现有的半监督学习技术，这是第一个困难的常见补救措施。在本文中，我们提出了一种实用的半监督学习方法，通过新开发两个额外的功能，在结肠中的位置（例如，左结肠）和图像捕获顺序，这两者通常被附加到内窥镜图像序列中的各个图像。该方法可以有效地提取UC分类的基本信息，通过一个解纠缠过程与这些功能。实验结果表明，该方法优于现有的几个半监督学习方法在分类任务，即使有少量的注释图像。
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.