1 research outputs found
End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification
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