Abdominal MRI synthesis using styleGAN2-ADA
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.21/16750 |
Resumo: | This work was funded by FCT—Portuguese Foundation for Science and Technology and Bee2Fire SA under the PhD grant with reference PD/BDE/150624/2020. |
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7160 |
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Abdominal MRI synthesis using styleGAN2-ADAMagnetic resonance imagingGenerative adversarial networksStyleGAN2Image synthesisMedical imagingDeep learningMalignant tumourPipelinesTraining dataThis work was funded by FCT—Portuguese Foundation for Science and Technology and Bee2Fire SA under the PhD grant with reference PD/BDE/150624/2020.The lack of labeled medical data still poses one of the biggest issues when creating Deep Learning models in the medical field. Modern data augmentation techniques like the generation of synthetic images have gained a special interest. In recent years there has been a significant improvement in GANs. StyleGAN2 achieves impressive results in the generation of natural images. StyleGAN2-ADA was created to respond to the lack of training data when training an image synthesis model, which is very frequent in the medical field. Some works used styleGAN to generate melanomas, breast cancer histological images, and MR and CT images. In this work, we apply, for the first time, a styleGAN2-ADA to a small dataset of abdominal MRI with 1.3k images. From the augmentation pipeline created by the authors of styleGAN2-ADA, we removed all augmentations except the geometric transformations and pixel blitting operations. We trained our network for 70 hours. Our generated dataset has a precision score of 59,33 % and a FID score of 18,14. We conclude that the styleGAN2-ADA is a viable solution to generate MRI using a small dataset.IEEERCIPLGonçalves, BernardoVieira, PedroVieira, Ana2023-072023-07-01T00:00:00Z2025-07-31T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/16750engGonçalves B, Vieira P, Vieira A (Ana). Abdominal MRI synthesis using styleGAN2-ADA. In: 2023 IST-Africa Conference (IST-Africa), Tshwane (South Africa), May 31 – June 02, 2023.10.23919/IST-Africa60249.2023.10187755info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-10T02:18:23Zoai:repositorio.ipl.pt:10400.21/16750Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:31:12.453697Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Abdominal MRI synthesis using styleGAN2-ADA |
title |
Abdominal MRI synthesis using styleGAN2-ADA |
spellingShingle |
Abdominal MRI synthesis using styleGAN2-ADA Gonçalves, Bernardo Magnetic resonance imaging Generative adversarial networks StyleGAN2 Image synthesis Medical imaging Deep learning Malignant tumour Pipelines Training data |
title_short |
Abdominal MRI synthesis using styleGAN2-ADA |
title_full |
Abdominal MRI synthesis using styleGAN2-ADA |
title_fullStr |
Abdominal MRI synthesis using styleGAN2-ADA |
title_full_unstemmed |
Abdominal MRI synthesis using styleGAN2-ADA |
title_sort |
Abdominal MRI synthesis using styleGAN2-ADA |
author |
Gonçalves, Bernardo |
author_facet |
Gonçalves, Bernardo Vieira, Pedro Vieira, Ana |
author_role |
author |
author2 |
Vieira, Pedro Vieira, Ana |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
RCIPL |
dc.contributor.author.fl_str_mv |
Gonçalves, Bernardo Vieira, Pedro Vieira, Ana |
dc.subject.por.fl_str_mv |
Magnetic resonance imaging Generative adversarial networks StyleGAN2 Image synthesis Medical imaging Deep learning Malignant tumour Pipelines Training data |
topic |
Magnetic resonance imaging Generative adversarial networks StyleGAN2 Image synthesis Medical imaging Deep learning Malignant tumour Pipelines Training data |
description |
This work was funded by FCT—Portuguese Foundation for Science and Technology and Bee2Fire SA under the PhD grant with reference PD/BDE/150624/2020. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07 2023-07-01T00:00:00Z 2025-07-31T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.21/16750 |
url |
http://hdl.handle.net/10400.21/16750 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Gonçalves B, Vieira P, Vieira A (Ana). Abdominal MRI synthesis using styleGAN2-ADA. In: 2023 IST-Africa Conference (IST-Africa), Tshwane (South Africa), May 31 – June 02, 2023. 10.23919/IST-Africa60249.2023.10187755 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799136793841369088 |