In recent years, many papers have reported state-of-the-art performance on Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural networks. However, we discover that when we split that data into training and testing sets at the subject level, we are not able to obtain similar performance, bringing the validity of many of the previous studies into question. Furthermore, we point out that previous works use different subsets of the ADNI data, making comparison across similar works tricky. In this study, we present the results of three splitting methods, discuss the motivations behind their validity, and report our results using all of the available subjects.
@cite_3: Automatic diagnosis of Alzheimers disease (AD) and mild cognition impairment (MCI) from 3D brain magnetic resonance (MR) images plays an important role in early treatment of dementia disease. Deep learning architectures can extract potential features of dementia disease and capture brain anatomical changes from MRI scans. This paper proposes an ensemble of 3D densely connected convolutional networks (3D-DenseNets) for AD and MCI diagnosis. First, dense connections were introduced to maximize the information flow, where each layer connects with all subsequent layers directly. Then weighted-based fusion method was employed to combine 3D-DenseNets with different architectures. Extensive experiments were conducted to analyze the performance of 3D-DenseNet with different hyper-parameters and architectures. Superior performance of the proposed model was demonstrated on ADNI dataset including 833 subjects.
More recently, @cite_3 trained a 3D DensetNet with ensemble voting on the ADNI dataset to achieve the state-of-the-art accuracy on three-class classification. They split by patients but treated MRIs of the same patient that are over three years apart as different patients, giving away some information from the training to testing process.