publication . Preprint . 2016

Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network

Hosseini-Asl, Ehsan; Gimel'farb, Georgy; El-Baz, Ayman;
Open Access English
  • Published: 02 Jul 2016
Abstract
Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus shape, cortical thickness, and brain volume. This paper proposes to predict the AD with a deep 3D convolutional neural network (3D-CNN), which can learn generic features capturing AD biomarkers and adapt to different domain datasets. The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations...
Subjects
free text keywords: Computer Science - Learning, Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning
Funded by
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
,
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
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