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.
|Title of host publication
|MRI field strength predicts Alzheimer's disease: a case example of bias in the ADNI data set
|Published - 26 Apr 2022
|IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India
Duration: 28 Mar 2022 → 31 Mar 2022
|IEEE International Symposium on Biomedical Imaging, ISBI 2022
|28/03/22 → 31/03/22