MRI field strength predicts Alzheimer's disease: a case example of bias in the ADNI data set

Elina Thibeau-Sutre, Baptiste Couvy-Duchesne, Didier Dormont, Olivier Colliot, Ninon Burgos

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

5 Citations (Scopus)
9 Downloads (Pure)

Abstract

The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set has been extensively used for the prediction of the progression of prodromal patients to Alzheimer's disease dementia. However, the deep learning community is not always aware of the biases that may contaminate neuroimaging data sets, which may lead to flawed results. In this case example, we demonstrated how ignoring the magnetic resonance (MR) field strength can bias performance of deep learning prediction when using MR images as input. Finally, we discussed options to overcome this problem.
Original languageEnglish
Title of host publicationMRI field strength predicts Alzheimer's disease: a case example of bias in the ADNI data set
ISBN (Electronic)978-1-6654-2923-8
DOIs
Publication statusPublished - 26 Apr 2022
Externally publishedYes
EventIEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India
Duration: 28 Mar 202231 Mar 2022

Conference

ConferenceIEEE International Symposium on Biomedical Imaging, ISBI 2022
Abbreviated titleISBI 2022
Country/TerritoryIndia
CityKolkata
Period28/03/2231/03/22

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