Endoscopic Image Clustering with Temporal Ordering Information Based on Dynamic Programming

Shota Harada, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, Qier Meng, Seiichi Uchida

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

3 被引用数 (Scopus)

抄録

In this paper, we propose a clustering method with temporal ordering information for endoscopic image sequences. It is difficult to collect a sufficient amount of endoscopic image datasets to train machine learning techniques by manual labeling. The clustering of endoscopic images leads to group-based labeling, which is useful for reducing the cost of dataset construction. Therefore, in this paper, we propose a clustering method where the property of endoscopic image sequences is fully utilized. For the proposed method, a deep neural network was used to extract features from endoscopic images, and clustering with temporal ordering information was solved by dynamic programming. In the experiments, we clustered the esophagogastroduodenoscopy images. From the results, we confirmed that the performance was improved by using the sequential property.

本文言語英語
ホスト出版物のタイトル2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3681-3684
ページ数4
ISBN(電子版)9781538613115
DOI
出版ステータス出版済み - 7 2019
イベント41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, ドイツ
継続期間: 7 23 20197 27 2019

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN(印刷版)1557-170X

会議

会議41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Countryドイツ
CityBerlin
Period7/23/197/27/19

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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