CT Scan Synthesis for Promoting Computer-Aided Diagnosis Capacity of COVID-19

Heng Li, Yan Hu, Sanqian Li, Wenjun Lin, Peng Liu, Risa Higashita, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Nowadays, with the rapid spread of Corona Virus Disease 2019 (COVID-19), this epidemic has become a threatening risk for global public health. Medical workers and researchers all over the world are struggling against the novel coronavirus in the front line. Because the computed tomography (CT) images from infected patients exposure characteristic abnormalities, automatic CT analyzers based on AI-based algorithms are extensively employed as effective weapons to aid clinicians. However, unbalanced data and lack of annotations obstruct AI-based algorithms applying in aided diagnosis because of their low performance. Therefore, in order to solve the above problems, a general-purpose solution is proposed to synthesize COVID-19 CT scans from non-COVID-19 data for providing high-quality negative-positive paired CT scans. Particularly, we introduce an elastic registration algorithm of CT images to manufacture paired training data. Then, a conditional Generative Adversarial Networks (GANs) based image-to-image translation model is implemented to synthesize COVID-19 CT scans from non-COVID-19 data. The effectiveness of our proposed algorithm used in COVID-19 aided diagnosis is verified in the experiments, and the identification and detection capacities of the classification models have been enhanced with the generated CT scans. Specifically, the precise lesion location is achieved by the generated data with a weakly supervised algorithm of class activation mapping (CAM). The model and code of this paper are publicly available at https://github.com/lihengbit/Synthesis-of-COVID-19-CT-Scan.

Original languageEnglish
Title of host publicationIntelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings
EditorsDe-Shuang Huang, Kang-Hyun Jo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages413-422
Number of pages10
ISBN (Print)9783030608019
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event16th International Conference on Intelligent Computing, ICIC 2020 - Bari , Italy
Duration: 2 Oct 20205 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12464 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Intelligent Computing, ICIC 2020
Country/TerritoryItaly
CityBari
Period2/10/205/10/20

Keywords

  • CT scan synthesis
  • Image-to-image translation
  • Lesion location
  • Weakly supervised

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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