  Medical image analysis using supervised deep learning methods remains
problematic because of the reliance of deep learning methods on large amounts
of labelled training data. Although medical imaging data repositories continue
to expand there has not been a commensurate increase in the amount of annotated
data. Hence, we propose a new unsupervised feature learning method that learns
feature representations to then differentiate dissimilar medical images using
an ensemble of different convolutional neural networks (CNNs) and K-means
clustering. It jointly learns feature representations and clustering
assignments in an end-to-end fashion. We tested our approach on a public
medical dataset and show its accuracy was better than state-of-the-art
unsupervised feature learning methods and comparable to state-of-the-art
supervised CNNs. Our findings suggest that our method could be used to tackle
the issue of the large volume of unlabelled data in medical imaging
repositories.
