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

@article{HosseiniAsl2016AlzheimersDD,
  title={Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional Network},
  author={Ehsan Hosseini-Asl and G. Gimel'farb and A. El-Baz},
  journal={ArXiv},
  year={2016},
  volume={abs/1607.00556}
}
  • Ehsan Hosseini-Asl, G. Gimel'farb, A. El-Baz
  • Published 2016
  • Computer Science, Biology, Mathematics
  • ArXiv
  • 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… CONTINUE READING
    105 Citations

    Figures, Tables, and Topics from this paper.

    3D Convolutional Neural Networks for Diagnosis of Alzheimer's Disease via Structural MRI
    Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network
    • 19
    • PDF
    An extended-2D CNN for multiclass Alzheimer's Disease diagnosis through structural MRI
    3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies
    • 42
    • PDF
    Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning
    • 19
    • Highly Influenced
    • PDF

    References

    SHOWING 1-10 OF 53 REFERENCES
    Predicting Alzheimer's Disease - A Neuroimaging Study with 3D Convolutional Neural Networks
    • 251
    • PDF
    Early diagnosis of Alzheimer's disease with deep learning
    • 220
    • PDF
    Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease
    • 245
    • PDF
    A Robust Deep Model for Improved Classification of AD/MCI Patients
    • 128
    Deep Learning-Based Feature Representation for AD/MCI Classification
    • 250
    • PDF
    Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
    • 418
    • PDF
    Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis
    • 69
    • PDF
    Natural Image Bases to Represent Neuroimaging Data
    • 128
    • PDF
    Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging
    • 328
    • PDF