  Increasing numbers of MRI brain scans, improvements in image resolution, and
advancements in MRI acquisition technology are causing significant increases in
the demand for and burden on radiologists' efforts in terms of reading and
interpreting brain MRIs. Content-based image retrieval (CBIR) is an emerging
technology for reducing this burden by supporting the reading of medical
images. High dimensionality is a major challenge in developing a CBIR system
that is applicable for 3D brain MRIs. In this study, we propose a system called
disease-oriented data concentration with metric learning (DDCML). In DDCML, we
introduce deep metric learning to a 3D convolutional autoencoder (CAE). Our
proposed DDCML scheme achieves a high dimensional compression rate (4096:1)
while preserving the disease-related anatomical features that are important for
medical image classification. The low-dimensional representation obtained by
DDCML improved the clustering performance by 29.1\% compared to plain 3D-CAE in
terms of discriminating Alzheimer's disease patients from healthy subjects, and
successfully reproduced the relationships of the severity of disease categories
that were not included in the training.
