Reducing Variations in Multi-center Alzheimer's Disease Diagnosis with
Federated Learning
 
   Lead Investigator:    Bernard Cobbinah
   Institution      :    UESTC, China
   E-Mail           :    cobbinahben@std.uestc.edu.cn
   Proposal ID      :    1577



   Proposal Description:
   The goals of this research are     (1) Harnessing the capabilities of deep learning in handling the problem of       multi-center MRI variations, and making Alzheimer's disease classification from   raw MRI data without the typical medical preprocessing pipeline.                  (2) To create a novel model with federated learning specifically for              Alzheimer's disease diagnosis to collaboratively use MRI data and clinical        records from different sources to enhance the disease diagnosis while             preserving data privacy in accordance with GDPR privacy laws, etc.