Weakly-supervised lesion analysis with a CNN-based framework for COVID-19

Kaichao Wu, Beth Jelfs, Xiangyuan Ma, Ruitian Ke, Xuerui Tan*, Qiang Fang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Objective. Lesions of COVID-19 can be clearly visualized using chest CT images, and hence provide valuable evidence for clinicians when making a diagnosis. However, due to the variety of COVID-19 lesions and the complexity of the manual delineation procedure, automatic analysis of lesions with unknown and diverse types from a CT image remains a challenging task. In this paper we propose a weakly-supervised framework for this task requiring only a series of normal and abnormal CT images without the need for annotations of the specific locations and types of lesions.

Approach. A deep learning-based diagnosis branch is employed for classification of the CT image and then a lesion identification branch is leveraged to capture multiple types of lesions.

Main Results. Our framework is verified on publicly available datasets and CT data collected from 13 patients of the First Affiliated Hospital of Shantou University Medical College, China. The results show that the proposed framework can achieve state-of-the-art diagnosis prediction, and the extracted lesion features are capable of distinguishing between lesions showing ground glass opacity and consolidation.

Significance. The proposed approach integrates COVID-19 positive diagnosis and lesion analysis into a unified framework without extra pixel-wise supervision. Further exploration also demonstrates that this framework has the potential to discover lesion types that have not been reported and can potentially be generalized to lesion detection of other chest-based diseases.

Original languageEnglish
Article number245027
Number of pages20
JournalPhysics in Medicine and Biology
Volume66
Issue number24
DOIs
Publication statusPublished - 31 Dec 2021
Externally publishedYes

Bibliographical note

Funding Information:
Special Grant from Department of Education of Guangdong 2020KZDZX1093 Li Ka Shing Foundation Cross-Disciplinary Research Grant 2020LKSFG01C

Publisher Copyright:
© 2021 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd.

Keywords

  • chest CT image
  • COVID-19
  • GGO
  • lesion identification
  • weakly-supervised

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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