The use of generative adversarial networks in medical image augmentation
<p dir="ltr">Generative Adversarial Networks (GANs) have been widely applied in various domains, including medical image analysis. GANs have been utilized in classification and segmentation tasks, aiding in the detection and diagnosis of diseases and disorders. However, medical image...
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| مؤلفون آخرون: | , , |
| منشور في: |
2023
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| _version_ | 1864513534771068928 |
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| author | Ahmed Makhlouf (17632179) |
| author2 | Marina Maayah (17707242) Nada Abughanam (14152413) Cagatay Catal (6897842) |
| author2_role | author author author |
| author_facet | Ahmed Makhlouf (17632179) Marina Maayah (17707242) Nada Abughanam (14152413) Cagatay Catal (6897842) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmed Makhlouf (17632179) Marina Maayah (17707242) Nada Abughanam (14152413) Cagatay Catal (6897842) |
| dc.date.none.fl_str_mv | 2023-10-15T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00521-023-09100-z |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/The_use_of_generative_adversarial_networks_in_medical_image_augmentation/24912174 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Generative adversarial networks GAN Medical image Image augmentation Systematic review Review |
| dc.title.none.fl_str_mv | The use of generative adversarial networks in medical image augmentation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Generative Adversarial Networks (GANs) have been widely applied in various domains, including medical image analysis. GANs have been utilized in classification and segmentation tasks, aiding in the detection and diagnosis of diseases and disorders. However, medical image datasets often suffer from insufficiency and imbalanced class distributions. To overcome these limitations, researchers have employed GANs to generate augmented medical images, effectively expanding datasets and balancing class distributions. This review follows the PRISMA guidelines and systematically collects peer-reviewed articles on the development of GAN-based augmentation models. Automated searches were conducted on electronic databases such as IEEE, Scopus, Science Direct, and PubMed, along with forward and backward snowballing. Out of numerous articles, 52 relevant ones published between 2018 and February 2022 were identified. The gathered information was synthesized to determine common GAN architectures, medical image modalities, body organs of interest, augmentation tasks, and evaluation metrics employed to assess model performance. Results indicated that cGAN and DCGAN were the most popular GAN architectures in the reviewed studies. Medical image modalities such as MRI, CT, X-ray, and ultrasound, along with body organs like the brain, chest, breast, and lung, were frequently used. Furthermore, the developed models were evaluated, and potential challenges and future directions for GAN-based medical image augmentation were discussed. This review presents a comprehensive overview of the current state-of-the-art in GAN-based medical image augmentation and emphasizes the potential advantages and challenges associated with GAN utilization in this domain.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-023-09100-z" target="_blank">https://dx.doi.org/10.1007/s00521-023-09100-z</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_6abea9f41998f1689f36465c4dfd1117 |
| identifier_str_mv | 10.1007/s00521-023-09100-z |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24912174 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The use of generative adversarial networks in medical image augmentationAhmed Makhlouf (17632179)Marina Maayah (17707242)Nada Abughanam (14152413)Cagatay Catal (6897842)EngineeringBiomedical engineeringInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationData management and data scienceGenerative adversarial networksGANMedical imageImage augmentationSystematic reviewReview<p dir="ltr">Generative Adversarial Networks (GANs) have been widely applied in various domains, including medical image analysis. GANs have been utilized in classification and segmentation tasks, aiding in the detection and diagnosis of diseases and disorders. However, medical image datasets often suffer from insufficiency and imbalanced class distributions. To overcome these limitations, researchers have employed GANs to generate augmented medical images, effectively expanding datasets and balancing class distributions. This review follows the PRISMA guidelines and systematically collects peer-reviewed articles on the development of GAN-based augmentation models. Automated searches were conducted on electronic databases such as IEEE, Scopus, Science Direct, and PubMed, along with forward and backward snowballing. Out of numerous articles, 52 relevant ones published between 2018 and February 2022 were identified. The gathered information was synthesized to determine common GAN architectures, medical image modalities, body organs of interest, augmentation tasks, and evaluation metrics employed to assess model performance. Results indicated that cGAN and DCGAN were the most popular GAN architectures in the reviewed studies. Medical image modalities such as MRI, CT, X-ray, and ultrasound, along with body organs like the brain, chest, breast, and lung, were frequently used. Furthermore, the developed models were evaluated, and potential challenges and future directions for GAN-based medical image augmentation were discussed. This review presents a comprehensive overview of the current state-of-the-art in GAN-based medical image augmentation and emphasizes the potential advantages and challenges associated with GAN utilization in this domain.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-023-09100-z" target="_blank">https://dx.doi.org/10.1007/s00521-023-09100-z</a></p>2023-10-15T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00521-023-09100-zhttps://figshare.com/articles/journal_contribution/The_use_of_generative_adversarial_networks_in_medical_image_augmentation/24912174CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249121742023-10-15T03:00:00Z |
| spellingShingle | The use of generative adversarial networks in medical image augmentation Ahmed Makhlouf (17632179) Engineering Biomedical engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Generative adversarial networks GAN Medical image Image augmentation Systematic review Review |
| status_str | publishedVersion |
| title | The use of generative adversarial networks in medical image augmentation |
| title_full | The use of generative adversarial networks in medical image augmentation |
| title_fullStr | The use of generative adversarial networks in medical image augmentation |
| title_full_unstemmed | The use of generative adversarial networks in medical image augmentation |
| title_short | The use of generative adversarial networks in medical image augmentation |
| title_sort | The use of generative adversarial networks in medical image augmentation |
| topic | Engineering Biomedical engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Data management and data science Generative adversarial networks GAN Medical image Image augmentation Systematic review Review |