  Transfer learning has been widely utilized to mitigate the data scarcity
problem in the field of Alzheimer's disease (AD). Conventional transfer
learning relies on re-using models trained on AD-irrelevant tasks such as
natural image classification. However, it often leads to negative transfer due
to the discrepancy between the non-medical source and target medical domains.
To address this, we present evidence-empowered transfer learning for AD
diagnosis. Unlike conventional approaches, we leverage an AD-relevant auxiliary
task, namely morphological change prediction, without requiring additional MRI
data. In this auxiliary task, the diagnosis model learns the evidential and
transferable knowledge from morphological features in MRI scans. Experimental
results demonstrate that our framework is not only effective in improving
detection performance regardless of model capacity, but also more
data-efficient and faithful.
