The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classification accuracy when an extremely small amount of labelled data is available has yet to be tackled. In this work, we introduce a novel semi-supervised framework for X-ray classification which is based on a graph-based optimisation model. To the best of our knowledge, this is the first method that exploits graph-based semi-supervised learning for X-ray data classification. Furthermore, we introduce a new multi-class classification functional with carefully selected class priors which allows for a smooth solution that strengthens the synergy between the limited number of labels and the huge amount of unlabelled data. We demonstrate, through a set of numerical and visual experiments, that our method produces highly competitive results on the ChestX-ray14 data set whilst drastically reducing the need for annotated data.
@article{arxiv.1907.10085,
title = {GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision},
author = {Angelica I. Aviles-Rivero and Nicolas Papadakis and Ruoteng Li and Philip Sellars and Qingnan Fan and Robby T. Tan and Carola-Bibiane Schönlieb},
journal= {arXiv preprint arXiv:1907.10085},
year = {2020}
}