Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.

Generative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This study aims to explore a...

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Detalles Bibliográficos
Autores: Montero A, Bonet-Carne E, Burgos-Artizzu XP
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Fundació Sant Joan de Déu
Repositorio:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
OAI Identifier:oai:fsjd.fundanetsuite.com:p20382
Acceso en línea:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20382
Access Level:acceso abierto
Palabra clave:deep learning
generative adversarial networks
ultrasound image classification
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spelling Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.Montero ABonet-Carne EBurgos-Artizzu XPdeep learninggenerative adversarial networksultrasound image classificationGenerative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This study aims to explore and evaluate the generation of synthetic ultrasound fetal brain images via GANs and apply them to improve fetal brain ultrasound plane classification. State of the art GANs stylegan2-ada were applied to fetal brain image generation and GAN-based data augmentation classifiers were compared with baseline classifiers. Our experimental results show that using data generated by both GANs and classical augmentation strategies allows for increasing the accuracy and area under the curve score.MDPI2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20382SENSORSISSN: 14248220reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déuinstname:Fundació Sant Joan de DéuInglésinfo:eu-repo/semantics/openAccessoai:fsjd.fundanetsuite.com:p203822026-05-27T12:37:41Z
dc.title.none.fl_str_mv Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.
title Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.
spellingShingle Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.
Montero A
deep learning
generative adversarial networks
ultrasound image classification
title_short Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.
title_full Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.
title_fullStr Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.
title_full_unstemmed Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.
title_sort Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.
dc.creator.none.fl_str_mv Montero A
Bonet-Carne E
Burgos-Artizzu XP
author Montero A
author_facet Montero A
Bonet-Carne E
Burgos-Artizzu XP
author_role author
author2 Bonet-Carne E
Burgos-Artizzu XP
author2_role author
author
dc.subject.none.fl_str_mv deep learning
generative adversarial networks
ultrasound image classification
topic deep learning
generative adversarial networks
ultrasound image classification
description Generative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream classification tasks. This study aims to explore and evaluate the generation of synthetic ultrasound fetal brain images via GANs and apply them to improve fetal brain ultrasound plane classification. State of the art GANs stylegan2-ada were applied to fetal brain image generation and GAN-based data augmentation classifiers were compared with baseline classifiers. Our experimental results show that using data generated by both GANs and classical augmentation strategies allows for increasing the accuracy and area under the curve score.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20382
url https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=20382
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv SENSORS
ISSN: 14248220
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reponame_str r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
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