  The success of deep learning in medical imaging is mostly achieved at the
cost of a large labeled data set. Semi-supervised learning (SSL) provides a
promising solution by leveraging the structure of unlabeled data to improve
learning from a small set of labeled data. Self-ensembling is a simple approach
used in SSL to encourage consensus among ensemble predictions of unknown
labels, improving generalization of the model by making it more insensitive to
the latent space. Currently, such an ensemble is obtained by randomization such
as dropout regularization and random data augmentation. In this work, we
hypothesize -- from the generalization perspective -- that self-ensembling can
be improved by exploiting the stochasticity of a disentangled latent space. To
this end, we present a stacked SSL model that utilizes unsupervised
disentangled representation learning as the stochastic embedding for
self-ensembling. We evaluate the presented model for multi-label classification
using chest X-ray images, demonstrating its improved performance over related
SSL models as well as the interpretability of its disentangled representations.
