Evidence-empowered Transfer Learning for Alzheimer's Disease

Understand

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.