A New Semi-Supervised Classification Method Using a Supervised Autoencoder for Biomedical Applications

Abstract

Annotation of biomedical databases by clinicians is a very difficult, sometimes imprecise, and time consuming task. An alternative is to ask the clinician expert for the annotations they are the most confident in, which results in a semi-supervised classification problem. In this paper, we present a new approach to solve semi-supervised classification tasks for biomedical applications, involving a supervised autoencoder network. 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. Experiments show that the SSAE outperforms Label Propagation and Spreading and the Fully Connected Neural Network both on a synthetic dataset and on four real-world biological datasets.