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Multi-task driven explainable diagnosis of COVID-19 using chest X-ray images.

Authors
  • Malhotra, Aakarsh1
  • Mittal, Surbhi2
  • Majumdar, Puspita1
  • Chhabra, Saheb1
  • Thakral, Kartik2
  • Vatsa, Mayank2
  • Singh, Richa2
  • Chaudhury, Santanu2
  • Pudrod, Ashwin3
  • Agrawal, Anjali4
  • 1 IIIT-Delhi, New Delhi 110020, India. , (India)
  • 2 IIT Jodhpur, 342037, India. , (India)
  • 3 Ashwini Hospital and Ramakant Heart Care Centre, 431602, India. , (India)
  • 4 TeleRadiology Solutions, 560048, India. , (India)
Type
Published Article
Journal
Pattern Recognition
Publisher
Elsevier
Publication Date
Feb 01, 2022
Volume
122
Pages
108243–108243
Identifiers
DOI: 10.1016/j.patcog.2021.108243
PMID: 34456368
Source
Medline
Keywords
Language
English
License
Unknown

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 (COMiT-Net) 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 and semantic segmentation of COVID19 symptoms in CXRs taken from the ChestXray-14, CheXpert, and a consolidated COVID-19 dataset. These annotations will be released to the research community. Experiments performed with more than 2500 frontal CXR images show that at 90% specificity, the proposed COMiT-Net yields 96.80% sensitivity. © 2021 Published by Elsevier Ltd.

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