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.
翻译:常规转让学习依靠的是重新使用经过有关AD相关任务培训的模型,例如自然图像分类;然而,由于非医疗来源与目标医疗领域之间的差异,这种学习往往导致负转移;为了解决这个问题,我们提出基于证据的转移学习,以便进行AD诊断;与常规方法不同,我们利用一个与AD相关的辅助任务,即形态变化预测,而不需要额外的MRI数据;在这项辅助任务中,诊断模型从MRI扫描中的形态特征中学习明显和可转移的知识;实验结果表明,我们的框架不仅在改进检测性能方面有效,不论模型能力如何,而且数据效率和忠诚性更高。</s>