Abstract：
Alzheimer\'s Disease is the most common cause of dementia, whose progression spans in different stages, from very mild cognitive impairment to mild and severe conditions. In clinical trials, Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are mostly used for the early diagnosis of neurodegenerative disorders since they provide volumetric and metabolic function information of the brain, respectively. In recent years, Deep Learning (DL) has been employed in medical imaging with promising results. Moreover, the use of the deep neural networks, especially Convolutional Neural Networks (CNNs), has also enabled the development of DL-based solutions in domains characterized by the need of leveraging information coming from multiple data sources, raising the Multimodal Deep Learning (MDL). In this paper, we conduct a systematic analysis of MDL approaches for dementia severity assessment exploiting MRI and PET scans. We propose a Multi Input-Multi Output 3D CNN whose training iterations change according to the characteristic of the input as it is able to handle incomplete acquisitions, in which one image modality is missed. Experiments performed on OASIS-3 dataset show the satisfactory results of the implemented network, which outperforms approaches exploiting both single image modality and different MDL fusion techniques.
摘要：
阿尔茨海默病是痴呆的最常见原因,他们的进展跨越不同的阶段，从非常轻度的认知障碍到轻度和严重的疾病。在临床试验中,磁共振成像（MRI）和正电子发射断层扫描（PET）主要用于神经退行性疾病的早期诊断，因为它们提供了大脑的体积和代谢功能信息。分别。近年来,深度学习（DL）已被用于医学成像，并取得了有希望的结果。此外，深度神经网络的使用，特别是卷积神经网络(CNN)，还支持在需要利用来自多个数据源的信息的领域中开发基于DL的解决方案，提升多模态深度学习(MDL)。在本文中,我们利用MRI和PET扫描对用于痴呆严重程度评估的MDL方法进行了系统分析.我们提出了一种多输入多输出3DCNN，其训练迭代根据输入的特征而变化，因为它能够处理不完整的采集，其中错过了一种图像模态。在OASIS-3数据集上进行的实验表明，所实现的网络具有令人满意的结果，它优于利用单一图像模态和不同MDL融合技术的方法。
