Umeå universitets logga

umu.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • 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 universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.ORCID-id: 0000-0003-4288-1208
Hamad Bin Khalifa University, Qatar Foundation, College of Science and Engineering, Doha, Qatar.
2023 (Engelska)Ingår i: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society, 2023, s. 659-667Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IEEE Computer Society, 2023. s. 659-667
Serie
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, ISSN 21607508, E-ISSN 21607516
Nationell ämneskategori
Medicinsk bildbehandling
Identifikatorer
URN: urn:nbn:se:umu:diva-214629DOI: 10.1109/CVPRW59228.2023.00073Scopus ID: 2-s2.0-85170829786ISBN: 9798350302493 (digital)OAI: oai:DiVA.org:umu-214629DiVA, id: diva2:1801014
Konferens
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023, Vancouver, Canada, 17-24 june, 2023.
Tillgänglig från: 2023-09-28 Skapad: 2023-09-28 Senast uppdaterad: 2023-09-28Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Person

Grönlund, Christer

Sök vidare i DiVA

Av författaren/redaktören
Grönlund, Christer
Av organisationen
Institutionen för strålningsvetenskaper
Medicinsk bildbehandling

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 78 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf