Paper Info
Title
Multi-Task Deep Learning Based Ct Imaging Analysis For Covid-19 Pneumonia: Classification And Segmentation
Abstract
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.
Year
DOI
Venue
2020
10.1016/j.compbiomed.2020.104037
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Deep learning, Multitask learning, Image classification, Image segmentation, Coronavirus (COVID-19), Computed tomography images
Journal
126
ISSN
Citations 
PageRank 
0010-4825
17
1.04
References 
Authors
0
4
Name
Order
Citations
PageRank
Amine Amyar1171.04
Romain Modzelewski2433.52
Hua Li3459.03
Ruan Su455953.00