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Medical image analysis using supervised deep learning methods remainsproblematic because of the reliance of deep learning methods on large amountsof labelled training data. Although medical imaging data repositories continueto expand there has not been a commensurate increase in the amount of annotateddata. Hence, we propose a new unsupervised feature learning method that learnsfeature representations to then differentiate dissimilar medical images usingan ensemble of different convolutional neural networks (CNNs) and K-meansclustering. It jointly learns feature representations and clusteringassignments in an end-to-end fashion. We tested our approach on a publicmedical dataset and show its accuracy was better than state-of-the-artunsupervised feature learning methods and comparable to state-of-the-artsupervised CNNs. Our findings suggest that our method could be used to tacklethe issue of the large volume of unlabelled data in medical imagingrepositories.

Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification