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Mild traumatic brain injury is a growing public health problem with anestimated incidence of over 1.7 million people annually in US. Diagnosis isbased on clinical history and symptoms, and accurate, concrete measures ofinjury are lacking. This work aims to directly use diffusion MR images obtainedwithin one month of trauma to detect injury, by incorporating deep learningtechniques. To overcome the challenge due to limited training data, we describeeach brain region using the bag of word representation, which specifies thedistribution of representative patch patterns. We apply a convolutionalauto-encoder to learn the patch-level features, from overlapping image patchesextracted from the MR images, to learn features from diffusion MR images ofbrain using an unsupervised approach. Our experimental results show that thebag of word representation using patch level features learnt by the autoencoder provides similar performance as that using the raw patch patterns, bothsignificantly outperform earlier work relying on the mean values of MR metricsin selected brain regions.

A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI