Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification
This addresses the challenge of limited annotated data in medical imaging, offering a potential solution for leveraging unlabeled repositories, though it is incremental as it builds on existing unsupervised and ensemble techniques.
The paper tackled the problem of medical image classification without large labeled datasets by proposing an unsupervised feature learning method combining K-means clustering and an ensemble of CNNs, achieving accuracy better than state-of-the-art unsupervised methods and comparable to supervised CNNs.
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.