Detecting emphysema with multiple instance learning

Silas Nyboe Orting, Jens Petersen, Laura H. Thomsen, Mathilde M.W. Wille, Marleen De Bruijne

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

10 Citations (Scopus)

Abstract

Emphysema is part of chronic obstructive pulmonary disease, a leading cause of mortality worldwide. Visual assessment of emphysema presence is useful for identifying subjects at risk and for research into disease development. We train a machine learning method to predict emphysema from visually assessed expert labels. We use a multiple instance learning approach to predict both scan-level and region-level emphysema presence. We evaluate performance on 600 low-dose CT scans from the Danish Lung Cancer Screening Study and achieve an AUC of 0.82 for scan-level prediction and AUCs between 0.76 and 0.88 for region-level prediction.

Original languageEnglish
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE
Publication date23 May 2018
Pages510-513
ISBN (Electronic)9781538636367
DOIs
Publication statusPublished - 23 May 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: 4 Apr 20187 Apr 2018

Conference

Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/TerritoryUnited States
CityWashington
Period04/04/201807/04/2018

Keywords

  • Emphysema
  • Multiple Instance Learning
  • Weak supervision

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