Class Imbalanced Medical Image Classification with Complication Data

Daiki Matsuno, Ryo Fujii, Hideo Saito

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

Abstract

This paper addresses the task of class-imbalanced medical image classification using complication labels. Medical image classification plays an essential role in improving patient care and reducing the burden on healthcare systems, along with other computer aided diagnosis tasks. However, there are two common obstacles with medical image image classification. One is that there is a high cost of acquiring annotations from doctors/experts. Two is that in many cases there is a differences in the number of incidents that appear per symptom which lead to class-imbalance. The above factors make medical image classification difficult to deal with. Additionally, there are cases where multiple symptoms appear in a mixed manner (complications), such as diabetic complications where both symptoms of infection and ischaemia could appear at the same time, which also could influence the classification result negatively. In this paper, a method that jointly tackles the obstacles mentioned above is presented. First, we conduct extensive experiment with various class-imbalanced learning methods introduced in previous works and also propose a method that improves the baseline class-imbalanced approaches by utilizing complication labels in pre-training. Second, we validate the proposed method with the diabetic foot ulcer dataset introduced in Diabetic Foot Ulcer Challenge 2021.

Original languageEnglish
Title of host publicationLifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages386-390
Number of pages5
ISBN (Electronic)9781665419048
DOIs
Publication statusPublished - 2022
Event4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022 - Osaka, Japan
Duration: 2022 Mar 72022 Mar 9

Publication series

NameLifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies

Conference

Conference4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022
Country/TerritoryJapan
CityOsaka
Period22/3/722/3/9

Keywords

  • class-imbalance
  • diabetic foot ulcers
  • medical image classification
  • semi-supervised learning
  • unlabeled data

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Instrumentation
  • Education

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