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

Autor: Anjali Agrawal, Surbhi Mittal, Ashwin Pudrod, Puspita Majumdar, Mayank Vatsa, Richa Singh, Saheb Chhabra, Kartik Thakral, Santanu Chaudhury, Aakarsh Malhotra
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
Computer Science - Machine Learning
Coronavirus disease 2019 (COVID-19)
Explainable Artificial Intelligence
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Multi-task learning
Limited availability
Machine learning
computer.software_genre
Article
Task (project management)
Machine Learning (cs.LG)
X-ray
Deep Learning
Artificial Intelligence
FOS: Electrical engineering
electronic engineering
information engineering
Segmentation
Diagnostics
Modalities
Modality (human–computer interaction)
business.industry
Deep learning
Image and Video Processing (eess.IV)
COVID-19
Electrical Engineering and Systems Science - Image and Video Processing
Detection
Signal Processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
computer
Software
Multi-task Learning
Zdroj: Pattern Recognition
ISSN: 0031-3203
Popis: 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.
Databáze: OpenAIRE
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