A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI

Shervin Minaee; Yao Wang; Anna Choromanska; Sohae Chung; Xiuyuan Wang; Els Fieremans; Steven Flanagan; Joseph Rath; Yvonne W Lui

arXiv · 1802.02925

A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI

Shervin Minaee·Yao Wang·Anna Choromanska·Sohae Chung·Xiuyuan Wang·Els Fieremans·Steven Flanagan·Joseph Rath·Yvonne W Lui

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Abstract

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.

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BibTeX RIS

Shervin Minaee, Yao Wang, Anna Choromanska, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui. 2018-04-11. A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI. https://arxiv.org/abs/1802.02925

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