Detecting emphysema with multiple instance learning

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

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
LandUnited States
ByWashington
Periode04/04/201807/04/2018

    Research areas

  • Emphysema, Multiple Instance Learning, Weak supervision

ID: 199968017