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

PATTERN RECOGNITION

卷:122

Article

Malhotra, Aakarsh¹  Mittal, Surbhi²  Majumdar, Puspita¹  Chhabra, Saheb¹  Thakral, Kartik²  Vatsa, Mayank²  Singh, Richa²  Chaudhury, Santanu²  Pudrod, Ashwin³  Agrawal, Anjali⁴ 

[1] IIT Delhi, New Delhi 110020, India

[2] IIT Jodhpur, Jodhpur 342037, Rajasthan, India

[3] Ashwini Hosp & Ramakant Heart Care Ctr, Nanded 431602, India

[4] TeleRadiol Solut, Bengaluru 560048, India

关键词: X-Ray;    COVID-19;    Detection;    Diagnostics;    Deep learning;    Explainable artificial intelligence;    Multi-task learning;   

DOI  :  10.1016/j.patcog.2021.108243

来源: Elsevier

【 摘 要 】

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. (c) 2021 Published by Elsevier Ltd.

【 授权许可】

Free