Cross-cohort dementia identification using transfer learning with FDG-PET imaging

Shen Lu, Yong Xia, Weidong Cai, David Dagan Feng, Michael Fulham

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Machine learning has recently been applied to the identification of dementia syndromes. Accurately labeled PET brain scans used to train machine learning models, however, are difficult to obtain in the clinical environment. Hence we propose a dementia classification method using transfer learning. The main focus of our research is to train a machine learning model using an accurately labeled source image cohort and an unlabeled target image cohort jointly, and then use this model to label the unlabeled target cohort. We show the effectiveness of this knowledge transfer approach by comparing the proposed method to several other methods on public and private image cohorts.

Original languageEnglish
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages1550-1554
Number of pages5
ISBN (Electronic)9781538636367
DOIs
StatePublished - 23 May 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: 4 Apr 20187 Apr 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/TerritoryUnited States
CityWashington
Period4/04/187/04/18

Keywords

  • Alzheimer's disease
  • Clustering
  • Dementia
  • FDG-PET
  • Transfer learning

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