HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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

Elina Thibeau-Sutre 1 Baptiste Couvy-Duchesne 1, 2 Didier Dormont 1, 3 Olivier Colliot 1 Ninon Burgos 1
1 ARAMIS - Algorithms, models and methods for images and signals of the human brain
SU - Sorbonne Université, Inria de Paris, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
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.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03542213
Contributor : Elina Thibeau-Sutre Connect in order to contact the contributor
Submitted on : Tuesday, January 25, 2022 - 11:11:45 AM
Last modification on : Thursday, May 19, 2022 - 9:39:39 AM
Long-term archiving on: : Tuesday, April 26, 2022 - 7:30:43 PM

File

ISBI_2022___ADNI_field_strengt...
Files produced by the author(s)

Identifiers

Citation

Elina Thibeau-Sutre, Baptiste Couvy-Duchesne, Didier Dormont, Olivier Colliot, Ninon Burgos. MRI field strength predicts Alzheimer's disease: a case example of bias in the ADNI data set. ISBI 2022 - International Symposium on Biomedical Imaging, Mar 2022, Kolkata, India. ⟨10.1109/ISBI52829.2022.9761504⟩. ⟨hal-03542213⟩

Share

Metrics

Record views

143

Files downloads

82