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

By Amine Amyar, Romain Modzelewski, Hua Li, Su Ruan. Comput. Biol. Medicine, 2020

470 citations recorded by the source corpus.

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.

Subfields: Infectious Diseases, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/871e2d99a4b84f7a8b2e71f45ea6979c1e0af423

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In Infectious Diseases

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Real-world data: a brief review of the methods, applications, challenges and opportunities (2022, methodology): “For example, ML is widely applied in in health informatics to generate RWE and formulate personalized healthcare [86–90] and was successfully employed on RWD collected during the COVID-19 pandemic to help understand the disease and evaluate its prevention and treatment strategies [91–95].”
Image Segmentation Using Deep Learning: A Survey (2020, background): “Last but not least, we should also mention DL-based segmentation techniques in biology [191] and evaluation of construction materials [192], which offer the opportunity to address highly relevant application domains but are subject to challenges related to the massive volume of the related image dat”