Evidence-empowered Transfer Learning for Alzheimer's Disease
This work addresses data scarcity and negative transfer issues in Alzheimer's disease diagnosis, offering a domain-specific improvement for medical imaging applications.
The paper tackled the problem of negative transfer in Alzheimer's disease diagnosis by introducing evidence-empowered transfer learning that uses an AD-relevant auxiliary task for morphological change prediction, resulting in improved detection performance, data efficiency, and faithfulness.
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.