# GraphXCOVID: Explainable Deep Graph Diffusion Pseudo-Labelling for Identifying COVID-19 on Chest X-rays

arXiv: https://arxiv.org/abs/2010.00378 · HTML: https://mlsanity.com/paper/2010.00378/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation](https://mlsanity.com/paper/2108.12204/) (2021)
- [Self-supervised deep convolutional neural network for chest X-ray classification](https://mlsanity.com/paper/2103.03055/) (2021)
- [Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels](https://mlsanity.com/paper/2202.07422/) (2022)
- [Graph Laplacian for Semi-Supervised Learning](https://mlsanity.com/paper/2301.04956/) (2023)
- [BASiS: Batch Aligned Spectral Embedding Space](https://mlsanity.com/paper/2211.16960/) (2022)
- [Deep learning for understanding multilabel imbalanced Chest X-ray datasets](https://mlsanity.com/paper/2207.14408/) (2022)

## Abstract (arXiv)

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.