Abstract

Background
Deep learning shows promise in detecting Alzheimer’s Disease (AD) based on brain MRI1. Models trained on limited samples may fail when applied to new datasets (other cohorts, scanners), but privacy concerns make it difficult to centralize large amounts of data2. To address this, we developed a general federated learning architecture3, which we applied to train a 3D convolutional neural network (CNN) to detect AD from T1‐weighted brain MRI data from the three independently collected phases of the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
Method
We trained a 3D‐CNN over a federated learning environment of 3 sites corresponding to each ADNI phases (ADNI1, ADNI2/GO, ADNI3). No subject data are shared across sites, only model parameters, thus satisfying privacy/regulatory requirements. Each site trains the model on its own local dataset for several epochs and shares only …

Metadata

publication

Alzheimer's & Dementia 19, e065998, 2023

year

2023

publication date

2023/6

authors

Dimitris Stripelis, Nikhil J Dhinagar, Rafael V Sanchez Romero, Sophia I Thomopoulos, Paul M Thompson, Jose Luis Ambite

link

https://alz-journals.onlinelibrary.wiley.com/doi/abs/10.1002/alz.065998

journal

Alzheimer's & Dementia

volume

19

pages

e065998