  Computer-aided diagnosis via deep learning relies on large-scale annotated
data sets, which can be costly when involving expert knowledge. Semi-supervised
learning (SSL) mitigates this challenge by leveraging unlabeled data. One
effective SSL approach is to regularize the local smoothness of neural
functions via perturbations around single data points. In this work, we argue
that regularizing the global smoothness of neural functions by filling the void
in between data points can further improve SSL. We present a novel SSL approach
that trains the neural network on linear mixing of labeled and unlabeled data,
at both the input and latent space in order to regularize different portions of
the network. We evaluated the presented model on two distinct medical image
data sets for semi-supervised classification of thoracic disease and skin
lesion, demonstrating its improved performance over SSL with local
perturbations and SSL with global mixing but at the input space only. Our code
is available at https://github.com/Prasanna1991/LatentMixing.
