Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

• Published in: Computers in biology and medicine Vol. 126; p. 104037
• Main Authors: Amyar, Amine, Modzelewski, Romain, Li, Hua, Ruan, Su
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-11-2020; Elsevier Limited; Elsevier
• Subjects: Accuracy; Artificial intelligence; Betacoronavirus; Chest; Classification; Coders; Cognitive tasks; Computed tomography; Computed tomography images; Coronavirus (COVID-19); Coronavirus Infections; Coronavirus Infections - diagnostic imaging; Coronaviruses; COVID-19; Datasets; Decoders; Deep Learning; Female; Humans; Image classification; Image processing; Image reconstruction; Image segmentation; Infections; Lesions; Life Sciences; Lung; Lung - diagnostic imaging; Lung cancer; Machine learning; Male; Medical imaging; Motivation; Multilayers; Multitask learning; Neural networks; Pandemics; Pneumonia; Pneumonia, Viral; Pneumonia, Viral - diagnostic imaging; SARS-CoV-2; Tomography, X-Ray Computed; Viral infections; Deep learning; Multitask learning; Image segmentation; Coronavirus (COVID-19); Computed tomography images; Image classification
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2020.104037

This paper presents an automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow-up the patients. In this work, we propose a new multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Three learning tasks: segmentation, classification and reconstruction are jointly performed with different datasets. Our motivation is on the one hand to leverage useful information contained in multiple related tasks to improve both segmentation and classification performances, and on the other hand to deal with the problems of small data because each task can have a relatively small dataset. Our architecture is composed of a common encoder for disentangled feature representation with three tasks, and two decoders and a multi-layer perceptron for reconstruction, segmentation and classification respectively. The proposed model is evaluated and compared with other image segmentation techniques using a dataset of 1369 patients including 449 patients with COVID-19, 425 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification.
•Multitask deep learning based model can be used to detect COVID-19 lesions on CT scans.•The proposed model can improve state of the art U-NET by leveraging useful information contained in multiple related tasks.•Obtained a dice coefficient of 88% for image segmentation and an accuracy of 94.67 for multiclass classification.•The proposed model can be used as a support tool to assist physicians.