Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Images

TL;DR

This paper introduces an end-to-end multi-task neural network for COVID-19 diagnosis from chest X-rays that also provides explainability through semantic segmentation, supported by a large annotated dataset.

Contribution

It presents a novel multi-task network for COVID-19 detection and segmentation, along with a large, manually annotated dataset of chest X-rays for research.

Findings

The network accurately predicts COVID-19 presence in X-rays.

Semantic segmentation highlights regions of interest for explainability.

A large annotated dataset is released for community use.

Abstract

With increasing number of COVID-19 cases globally, all the countries are ramping up the testing numbers. While the RT-PCR kits are available in sufficient quantity in several countries, others are facing challenges with limited availability of testing kits and processing centers in remote areas. This has motivated researchers to find alternate methods of testing which are reliable, easily accessible and faster. Chest X-Ray is one of the modalities that is gaining acceptance as a screening modality. Towards this direction, the paper has two primary contributions. Firstly, we present the COVID-19 Multi-Task Network which is an automated end-to-end network for COVID-19 screening. The proposed network not only predicts whether the CXR has COVID-19 features present or not, it also performs semantic segmentation of the regions of interest to make the model explainable. Secondly, with the help of medical professionals, we manually annotate the lung regions of 9000 frontal chest radiographs taken from ChestXray-14, CheXpert and a consolidated COVID-19 dataset. Further, 200 chest radiographs pertaining to COVID-19 patients are also annotated for semantic segmentation. This database will be released to the research community.