  In this paper we present a new approach to solve semi-supervised
classification tasks for biomedical applications, involving a supervised
autoencoder network. We create a network architecture that encodes labels into
the latent space of an autoencoder, and define a global criterion combining
classification and reconstruction losses. We train the Semi-Supervised
AutoEncoder (SSAE) on labelled data using a double descent algorithm. Then, we
classify unlabelled samples using the learned network thanks to a softmax
classifier applied to the latent space which provides a classification
confidence score for each class.
  We implemented our SSAE method using the PyTorch framework for the model,
optimizer, schedulers, and loss functions. We compare our semi-supervised
autoencoder method (SSAE) with classical semi-supervised methods such as Label
Propagation and Label Spreading, and with a Fully Connected Neural Network
(FCNN). Experiments show that the SSAE outperforms Label Propagation and
Spreading and the Fully Connected Neural Network both on a synthetic dataset
and on two real-world biological datasets.
