  Can one learn to diagnose COVID-19 under extreme minimal supervision? Since
the outbreak of the novel COVID-19 there has been a rush for developing
Artificial Intelligence techniques for expert-level disease identification on
Chest X-ray data. In particular, the use of deep supervised learning has become
the go-to paradigm. However, the performance of such models is heavily
dependent on the availability of a large and representative labelled dataset.
The creation of which is a heavily expensive and time consuming task, and
especially imposes a great challenge for a novel disease. Semi-supervised
learning has shown the ability to match the incredible performance of
supervised models whilst requiring a small fraction of the labelled examples.
This makes the semi-supervised paradigm an attractive option for identifying
COVID-19. In this work, we introduce a graph based deep semi-supervised
framework for classifying COVID-19 from chest X-rays. Our framework introduces
an optimisation model for graph diffusion that reinforces the natural relation
among the tiny labelled set and the vast unlabelled data. We then connect the
diffusion prediction output as pseudo-labels that are used in an iterative
scheme in a deep net. We demonstrate, through our experiments, that our model
is able to outperform the current leading supervised model with a tiny fraction
of the labelled examples. Finally, we provide attention maps to accommodate the
radiologist's mental model, better fitting their perceptual and cognitive
abilities. These visualisation aims to assist the radiologist in judging
whether the diagnostic is correct or not, and in consequence to accelerate the
decision.
