Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders

@article{MartnezMurcia2020StudyingTM,
  title={Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders},
  author={Francisco J. Mart{\'i}nez-Murcia and Andr{\'e}s Ortiz and Juan Manuel G{\'o}rriz and Javier Ram{\'i}rez and Diego Castillo-Barnes},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2020},
  volume={24},
  pages={17-26}
}
Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has become possible to extract high-level abstract features directly from MRI images that internally describe the distribution of data in low-dimensional manifolds. In this work, we try a new exploratory data… 

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