  We propose a Deep Variational Clustering (DVC) framework for unsupervised
representation learning and clustering of large-scale medical images. DVC
simultaneously learns the multivariate Gaussian posterior through the
probabilistic convolutional encoder and the likelihood distribution with the
probabilistic convolutional decoder; and optimizes cluster labels assignment.
Here, the learned multivariate Gaussian posterior captures the latent
distribution of a large set of unlabeled images. Then, we perform unsupervised
clustering on top of the variational latent space using a clustering loss. In
this approach, the probabilistic decoder helps to prevent the distortion of
data points in the latent space and to preserve the local structure of data
generating distribution. The training process can be considered as a
self-training process to refine the latent space and simultaneously optimizing
cluster assignments iteratively. We evaluated our proposed framework on three
public datasets that represented different medical imaging modalities. Our
experimental results show that our proposed framework generalizes better across
different datasets. It achieves compelling results on several medical imaging
benchmarks. Thus, our approach offers potential advantages over conventional
deep unsupervised learning in real-world applications. The source code of the
method and all the experiments are available publicly at:
https://github.com/csfarzin/DVC
