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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 26th Annual Conference, MIUA 2022, Proceedings
EditorsGuang Yang, Angelica Aviles-Rivero, Michael Roberts, Carola-Bibiane Schönlieb
PublisherSpringer Science and Business Media Deutschland GmbH
Pages609-622
Number of pages14
ISBN (Print)9783031120527
DOIs
Publication statusPublished - 2022
Event26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022 - Cambridge, United Kingdom
Duration: Jul 27 2022Jul 29 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13413 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022
Country/TerritoryUnited Kingdom
CityCambridge
Period7/27/227/29/22

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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