  Effective and accurate diagnosis of Alzheimer's disease (AD) or mild
cognitive impairment (MCI) can be critical for early treatment and thus has
attracted more and more attention nowadays. Since first introduced, machine
learning methods have been gaining increasing popularity for AD related
research. Among the various identified biomarkers, magnetic resonance imaging
(MRI) are widely used for the prediction of AD or MCI. However, before a
machine learning algorithm can be applied, image features need to be extracted
to represent the MRI images. While good representations can be pivotal to the
classification performance, almost all the previous studies typically rely on
human labelling to find the regions of interest (ROI) which may be correlated
to AD, such as hippocampus, amygdala, precuneus, etc. This procedure requires
domain knowledge and is costly and tedious.
  Instead of relying on extraction of ROI features, it is more promising to
remove manual ROI labelling from the pipeline and directly work on the raw MRI
images. In other words, we can let the machine learning methods to figure out
these informative and discriminative image structures for AD classification. In
this work, we propose to learn deep convolutional image features using
unsupervised and supervised learning. Deep learning has emerged as a powerful
tool in the machine learning community and has been successfully applied to
various tasks. We thus propose to exploit deep features of MRI images based on
a pre-trained large convolutional neural network (CNN) for AD and MCI
classification, which spares the effort of manual ROI annotation process.
