Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Leveraging GANs for data scarcity of COVID-19: Beyond the hype
Hamad Bin Khalifa University, Qatar Foundation, College of Science and Engineering, Doha, Qatar.
Umeå University, Faculty of Medicine, Department of Radiation Sciences.ORCID iD: 0000-0003-4288-1208
Hamad Bin Khalifa University, Qatar Foundation, College of Science and Engineering, Doha, Qatar.
2023 (English)In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society, 2023, p. 659-667Conference paper, Published paper (Refereed)
Abstract [en]

Artificial Intelligence (AI)-based models can help in diagnosing COVID-19 from lung CT scans and X-ray images; however, these models require large amounts of data for training and validation. Many researchers studied Generative Adversarial Networks (GANs) for producing synthetic lung CT scans and X-Ray images to improve the performance of AI-based models. It is not well explored how good GAN-based methods performed to generate reliable synthetic data. This work analyzes 43 published studies that reported GANs for synthetic data generation. Many of these studies suffered data bias, lack of reproducibility, and lack of feedback from the radiologists or other domain experts. A common issue in these studies is the unavailability of the source code, hindering reproducibility. The included studies reported rescaling of the input images to train the existing GANs architecture without providing clinical insights on how the rescaling was motivated. Finally, even though GAN-based methods have the potential for data augmentation and improving the training of AI-based models, these methods fall short in terms of their use in clinical practice. This paper highlights research hotspots in countering the data scarcity problem, identifies various issues as well as potentials, and provides recommendations to guide future research. These recommendations might be useful to improve acceptability for the GAN-based approaches for data augmentation as GANs for data augmentation are increasingly becoming popular in the AI and medical imaging research community.

Place, publisher, year, edition, pages
IEEE Computer Society, 2023. p. 659-667
Series
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, ISSN 21607508, E-ISSN 21607516
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:umu:diva-214629DOI: 10.1109/CVPRW59228.2023.00073Scopus ID: 2-s2.0-85170829786ISBN: 9798350302493 (electronic)OAI: oai:DiVA.org:umu-214629DiVA, id: diva2:1801014
Conference
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023, Vancouver, Canada, 17-24 june, 2023.
Available from: 2023-09-28 Created: 2023-09-28 Last updated: 2023-09-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Grönlund, Christer

Search in DiVA

By author/editor
Grönlund, Christer
By organisation
Department of Radiation Sciences
Medical Image Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 70 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf