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Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network
The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and high-resolution chest radiographs (CXRs) using progressive growing GAN (PGGAN). We trained two PGGAN models using normal and abnormal CXRs, solely r...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911730/ https://www.ncbi.nlm.nih.gov/pubmed/36759636 http://dx.doi.org/10.1038/s41598-023-28175-1 |
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author | Jang, Miso Bae, Hyun-jin Kim, Minjee Park, Seo Young Son, A-yeon Choi, Se Jin Choe, Jooae Choi, Hye Young Hwang, Hye Jeon Noh, Han Na Seo, Joon Beom Lee, Sang Min Kim, Namkug |
author_facet | Jang, Miso Bae, Hyun-jin Kim, Minjee Park, Seo Young Son, A-yeon Choi, Se Jin Choe, Jooae Choi, Hye Young Hwang, Hye Jeon Noh, Han Na Seo, Joon Beom Lee, Sang Min Kim, Namkug |
author_sort | Jang, Miso |
collection | PubMed |
description | The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and high-resolution chest radiographs (CXRs) using progressive growing GAN (PGGAN). We trained two PGGAN models using normal and abnormal CXRs, solely relying on normal CXRs to demonstrate the quality of synthetic CXRs that were 1000 × 1000 pixels in size. Image Turing tests were evaluated by six radiologists in a binary fashion using two independent validation sets to judge the authenticity of each CXR, with a mean accuracy of 67.42% and 69.92% for the first and second trials, respectively. Inter-reader agreements were poor for the first (κ = 0.10) and second (κ = 0.14) Turing tests. Additionally, a convolutional neural network (CNN) was used to classify normal or abnormal CXR using only real images and/or synthetic images mixed datasets. The accuracy of the CNN model trained using a mixed dataset of synthetic and real data was 93.3%, compared to 91.0% for the model built using only the real data. PGGAN was able to generate CXRs that were identical to real CXRs, and this showed promise to overcome imbalances between classes in CNN training. |
format | Online Article Text |
id | pubmed-9911730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99117302023-02-11 Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network Jang, Miso Bae, Hyun-jin Kim, Minjee Park, Seo Young Son, A-yeon Choi, Se Jin Choe, Jooae Choi, Hye Young Hwang, Hye Jeon Noh, Han Na Seo, Joon Beom Lee, Sang Min Kim, Namkug Sci Rep Article The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and high-resolution chest radiographs (CXRs) using progressive growing GAN (PGGAN). We trained two PGGAN models using normal and abnormal CXRs, solely relying on normal CXRs to demonstrate the quality of synthetic CXRs that were 1000 × 1000 pixels in size. Image Turing tests were evaluated by six radiologists in a binary fashion using two independent validation sets to judge the authenticity of each CXR, with a mean accuracy of 67.42% and 69.92% for the first and second trials, respectively. Inter-reader agreements were poor for the first (κ = 0.10) and second (κ = 0.14) Turing tests. Additionally, a convolutional neural network (CNN) was used to classify normal or abnormal CXR using only real images and/or synthetic images mixed datasets. The accuracy of the CNN model trained using a mixed dataset of synthetic and real data was 93.3%, compared to 91.0% for the model built using only the real data. PGGAN was able to generate CXRs that were identical to real CXRs, and this showed promise to overcome imbalances between classes in CNN training. Nature Publishing Group UK 2023-02-09 /pmc/articles/PMC9911730/ /pubmed/36759636 http://dx.doi.org/10.1038/s41598-023-28175-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Jang, Miso Bae, Hyun-jin Kim, Minjee Park, Seo Young Son, A-yeon Choi, Se Jin Choe, Jooae Choi, Hye Young Hwang, Hye Jeon Noh, Han Na Seo, Joon Beom Lee, Sang Min Kim, Namkug Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network |
title | Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network |
title_full | Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network |
title_fullStr | Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network |
title_full_unstemmed | Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network |
title_short | Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network |
title_sort | image turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911730/ https://www.ncbi.nlm.nih.gov/pubmed/36759636 http://dx.doi.org/10.1038/s41598-023-28175-1 |
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