  Mild traumatic brain injury is a growing public health problem with an
estimated incidence of over 1.7 million people annually in US. Diagnosis is
based on clinical history and symptoms, and accurate, concrete measures of
injury are lacking. This work aims to directly use diffusion MR images obtained
within one month of trauma to detect injury, by incorporating deep learning
techniques. To overcome the challenge due to limited training data, we describe
each brain region using the bag of word representation, which specifies the
distribution of representative patch patterns. We apply a convolutional
auto-encoder to learn the patch-level features, from overlapping image patches
extracted from the MR images, to learn features from diffusion MR images of
brain using an unsupervised approach. Our experimental results show that the
bag of word representation using patch level features learnt by the auto
encoder provides similar performance as that using the raw patch patterns, both
significantly outperform earlier work relying on the mean values of MR metrics
in selected brain regions.
