  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.
