Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data

Takeaki Kadota, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, Seiichi Uchida

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

Automatic image-based disease severity estimation generally uses discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult due to the images with ambiguous severity. An easier alternative is to use relative annotation, which compares the severity level between image pairs. By using a learning-to-rank framework with relative annotation, we can train a neural network that estimates rank scores that are relative to severity levels. However, the relative annotation for all possible pairs is prohibitive, and therefore, appropriate sample pair selection is mandatory. This paper proposes a deep Bayesian active-learning-to-rank, which trains a Bayesian convolutional neural network while automatically selecting appropriate pairs for relative annotation. We confirmed the efficiency of the proposed method through experiments on endoscopic images of ulcerative colitis. In addition, we confirmed that our method is useful even with the severe class imbalance because of its ability to select samples from minor classes automatically.

本文言語英語
ホスト出版物のタイトルMedical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings
編集者Guang Yang, Angelica Aviles-Rivero, Michael Roberts, Carola-Bibiane Schönlieb
出版社Springer Science and Business Media Deutschland GmbH
ページ609-622
ページ数14
ISBN(印刷版)9783031120527
DOI
出版ステータス出版済み - 2022
イベント26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022 - Cambridge, 英国
継続期間: 7月 27 20227月 29 2022

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13413 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022
国/地域英国
CityCambridge
Period7/27/227/29/22

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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