Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3008-3012. doi: 10.1109/EMBC46164.2021.9629587.
Alzheimer's disease (AD) is a non-treatable and non-reversible disease that affects about 6% of people who are 65 and older. Brain magnetic resonance imaging (MRI) is a pseudo-3D imaging technology that is widely used for AD diagnosis. Convolutional neural networks with 3D kernels (3D CNNs) are often the default choice for deep learning based MRI analysis. However, 3D CNNs are usually computationally costly and data-hungry. Such disadvantages post a barrier of using modern deep learning techniques in the medical imaging domain, in which the number of data that can be used for training is usually limited. In this work, we propose three approaches that leverage 2D CNNs on 3D MRI data. We test the proposed methods on the Alzheimer's Disease Neuroimaging Initiative dataset across two popular 2D CNN architectures. The evaluation results show that the proposed method improves the model performance on AD diagnosis by 8.33% accuracy or 10.11% auROC compared with the ResNet-based 3D CNN model, while significantly reducing the training time by over 89%. We also discuss the potential causes for performance improvement and the limitations. We believe this work can serve as a strong baseline for future researchers.
阿尔茨海默病（AD）是一种无法治愈和无法逆转的疾病，影响着大约 65 岁及以上人群的 6%。脑磁共振成像（MRI）是一种广泛用于 AD 诊断的伪三维成像技术。具有三维内核的卷积神经网络（3D CNN）通常是基于深度学习的 MRI 分析的默认选择。然而，3D CNN 通常计算成本高且数据需求大。这些缺点在医学成像领域使用现代深度学习技术时构成了障碍，因为用于训练的数据数量通常有限。在这项工作中，我们提出了三种利用 3D MRI 数据上的 2D CNN 的方法。我们在两个流行的 2D CNN 架构上对阿尔茨海默病神经影像学倡议数据集进行了测试。评估结果表明，与基于 ResNet 的 3D CNN 模型相比，所提出的方法将 AD 诊断的模型性能提高了 8.33%的准确率或 10.11%的 AUROC，同时训练时间显著减少了 89%以上。我们还讨论了性能提升的潜在原因和局限性。我们相信这项工作可以为未来的研究人员提供一个强有力的基准。
