  Interpretation of medical images for diagnosis and treatment of complex
disease from high-dimensional and heterogeneous data remains a key challenge in
transforming healthcare. In the last few years, both supervised and
unsupervised deep learning achieved promising results in the area of medical
imaging and image analysis. Unlike supervised learning which is biased towards
how it is being supervised and manual efforts to create class label for the
algorithm, unsupervised learning derive insights directly from the data itself,
group the data and help to make data driven decisions without any external
bias. This review systematically presents various unsupervised models applied
to medical image analysis, including autoencoders and its several variants,
Restricted Boltzmann machines, Deep belief networks, Deep Boltzmann machine and
Generative adversarial network. Future research opportunities and challenges of
unsupervised techniques for medical image analysis have also been discussed.
