Oct 31, 2018

Deep Learning on MRI Affirms the Prominence of the Hippocampal Formation in Alzheimer's Disease Classification

BioRxiv : the Preprint Server for Biology
Xinyang FengFrank A Provenzano

Abstract

Deep learning techniques on MRI scans have demonstrated great potential to improve the diagnosis of neurological diseases. Here, we investigate the application of 3D deep convolutional neural networks (CNNs) for classifying Alzheimer's disease (AD) based on structural MRI data. In particular, we take on two challenges that are under-explored in the literature on deep learning for neuroimaging. First deep neural networks typically require large-scale data that is not always available in medical studies. Therefore, we explore the use of including longitudinal scans in classification studies, greatly increasing the amount of data for training and improving the generalization performance of our classifiers. Moreover, previous studies applying deep learning to classifying Alzheimer's disease from neuroimaging have typically addressed classification based on whole brain volumes but stopped short of performing in-depth regional analyses to localize the most predictive areas. Additionally, we show a deep net trained to distinguish between AD and cognitively normal subjects can be applied to classify mild cognitive impairment patients, with classification scores aligning empirically with the likelihood of progression to AD. Our initial ...Continue Reading

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Mentioned in this Paper

Study
Biological Neural Networks
Classification
Ablation
Magnetic Resonance Imaging
Three-dimensional
Brain
Alzheimer's Disease
Entire Entorhinal Cortex
Neural Network Simulation

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