# Evidence-empowered Transfer Learning for Alzheimer's Disease

Source: [Evidence-empowered Transfer Learning for Alzheimer's Disease](<https://www.sota2.com/research/paper/68fa6f141e209131cea43bfd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:13:37.852Z

Authors: Kai Tzu-iunn Ong, Hana Kim, Minjin Kim, Jinseong Jang, Beomseok Sohn, Yoon Seong Choi, Dosik Hwang, Seong Jae Hwang, Jinyoung Yeo

Published: 2023-03-02T09:37:56.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.01105>)

[PDF](<https://arxiv.org/pdf/2303.01105.pdf>)