Esmaeilzadeh Soheil, Belivanis Dimitrios Ioannis, Pohl Kilian M, Adeli Ehsan
Stanford University.
SRI International.
Mach Learn Med Imaging. 2018 Sep;11046:337-345. doi: 10.1007/978-3-030-00919-9_39. Epub 2018 Sep 15.
As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods.
如计算机视觉所示，深度学习的强大之处在于能够为任何预测任务自动学习相关且强大的特征，这通过端到端架构得以实现。然而，应用于医学图像分类的深度学习方法并不遵循这种架构，因为它们依赖于多个预处理和后处理步骤。这种缺点可以通过可用标记受试者数量相对较少、神经成像数据的高维度以及解释深度学习方法结果的困难来解释。在本文中，我们提出了一种简单的三维卷积神经网络，并利用其模型参数来定制用于诊断阿尔茨海默病（AD）的端到端架构。我们的模型仅使用磁共振成像（MRI）数据在流行的阿尔茨海默病神经成像计划（ADNI）数据集上诊断AD的准确率可达94.1%，优于先前的最先进技术。基于所学模型，我们识别出疾病生物标志物，其结果与文献一致。我们进一步将所学模型用于诊断轻度认知障碍（MCI），即AD的前驱阶段，与其他方法相比产生了更好的结果。
