  Deep sparse auto-encoders with mixed structure regularization (MSR) in
addition to explicit sparsity regularization term and stochastic corruption of
the input data with Gaussian noise have the potential to improve unsupervised
abnormality detection. Unsupervised abnormality detection based on identifying
outliers using deep sparse auto-encoders is a very appealing approach for
medical computer aided detection systems as it requires only healthy data for
training rather than expert annotated abnormality. In the task of detecting
coronary artery disease from Coronary Computed Tomography Angiography (CCTA),
our results suggests that the MSR has the potential to improve overall
performance by 20-30% compared to deep sparse and denoising auto-encoders.
