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:转移学习已被广泛应用于缓解阿尔茨海默病(AD)领域中数据稀缺的问题。传统的转移学习依赖于在AD不相关的任务(如自然图像分类)上训练模型。然而,由于非医学源和目标医学领域之间的差异,它经常导致负迁移。为了解决这个问题,我们提出了一种用于AD诊断的证据驱动转移学习方法。与传统方法不同,我们利用一种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。在这个辅助任务中,诊断模型从MRI扫描的形态特征中学习证明和可转移的知识。实验结果表明,我们的框架不仅在提高检测性能方面有效(无论模型容量如何),而且更具数据效率性和准确性。