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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.
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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


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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⟩



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