Deep Clustering For Screening Diabetic Retinopathy

Abstract

Deep neural networks have been investigated in learning latent representations of medical images, yet most of the studies limit their approach using supervised convolutional neural network (CNN), which usually rely heavily on a large scale annotated dataset for training. To learn image representations with less supervision involved, we propose a deep clustering algorithm for learning latent representations of medical images. In this work, we present Deep clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. We iteratively groups the features with a standard clustering algorithm, k-means and uses the subsequent assignments as a supervision to update the weights of the network. We evaluated the learned image representations on a task of classi cation using a publicly available diabetic retinopathy fundus image dataset. The experimental results show that our proposed method is close to the state-of-the-art supervised ensemble CNN.