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BRAX, Brazilian labeled chest x-ray dataset
Chest radiographs allow for the meticulous examination of a patient’s chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) algorithms. Large labeled datasets are key element...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364309/ https://www.ncbi.nlm.nih.gov/pubmed/35948551 http://dx.doi.org/10.1038/s41597-022-01608-8 |
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author | Reis, Eduardo P. de Paiva, Joselisa P. Q. da Silva, Maria C. B. Ribeiro, Guilherme A. S. Paiva, Victor F. Bulgarelli, Lucas Lee, Henrique M. H. Santos, Paulo V. Brito, Vanessa M. Amaral, Lucas T. W. Beraldo, Gabriel L. Haidar Filho, Jorge N. Teles, Gustavo B. S. Szarf, Gilberto Pollard, Tom Johnson, Alistair E. W. Celi, Leo A. Amaro, Edson |
author_facet | Reis, Eduardo P. de Paiva, Joselisa P. Q. da Silva, Maria C. B. Ribeiro, Guilherme A. S. Paiva, Victor F. Bulgarelli, Lucas Lee, Henrique M. H. Santos, Paulo V. Brito, Vanessa M. Amaral, Lucas T. W. Beraldo, Gabriel L. Haidar Filho, Jorge N. Teles, Gustavo B. S. Szarf, Gilberto Pollard, Tom Johnson, Alistair E. W. Celi, Leo A. Amaro, Edson |
author_sort | Reis, Eduardo P. |
collection | PubMed |
description | Chest radiographs allow for the meticulous examination of a patient’s chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) algorithms. Large labeled datasets are key elements for training and validation of these ML solutions. In this paper we describe the Brazilian labeled chest x-ray dataset, BRAX: an automatically labeled dataset designed to assist researchers in the validation of ML models. The dataset contains 24,959 chest radiography studies from patients presenting to a large general Brazilian hospital. A total of 40,967 images are available in the BRAX dataset. All images have been verified by trained radiologists and de-identified to protect patient privacy. Fourteen labels were derived from free-text radiology reports written in Brazilian Portuguese using Natural Language Processing. |
format | Online Article Text |
id | pubmed-9364309 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93643092022-08-10 BRAX, Brazilian labeled chest x-ray dataset Reis, Eduardo P. de Paiva, Joselisa P. Q. da Silva, Maria C. B. Ribeiro, Guilherme A. S. Paiva, Victor F. Bulgarelli, Lucas Lee, Henrique M. H. Santos, Paulo V. Brito, Vanessa M. Amaral, Lucas T. W. Beraldo, Gabriel L. Haidar Filho, Jorge N. Teles, Gustavo B. S. Szarf, Gilberto Pollard, Tom Johnson, Alistair E. W. Celi, Leo A. Amaro, Edson Sci Data Data Descriptor Chest radiographs allow for the meticulous examination of a patient’s chest but demands specialized training for proper interpretation. Automated analysis of medical imaging has become increasingly accessible with the advent of machine learning (ML) algorithms. Large labeled datasets are key elements for training and validation of these ML solutions. In this paper we describe the Brazilian labeled chest x-ray dataset, BRAX: an automatically labeled dataset designed to assist researchers in the validation of ML models. The dataset contains 24,959 chest radiography studies from patients presenting to a large general Brazilian hospital. A total of 40,967 images are available in the BRAX dataset. All images have been verified by trained radiologists and de-identified to protect patient privacy. Fourteen labels were derived from free-text radiology reports written in Brazilian Portuguese using Natural Language Processing. Nature Publishing Group UK 2022-08-10 /pmc/articles/PMC9364309/ /pubmed/35948551 http://dx.doi.org/10.1038/s41597-022-01608-8 Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Reis, Eduardo P. de Paiva, Joselisa P. Q. da Silva, Maria C. B. Ribeiro, Guilherme A. S. Paiva, Victor F. Bulgarelli, Lucas Lee, Henrique M. H. Santos, Paulo V. Brito, Vanessa M. Amaral, Lucas T. W. Beraldo, Gabriel L. Haidar Filho, Jorge N. Teles, Gustavo B. S. Szarf, Gilberto Pollard, Tom Johnson, Alistair E. W. Celi, Leo A. Amaro, Edson BRAX, Brazilian labeled chest x-ray dataset |
title | BRAX, Brazilian labeled chest x-ray dataset |
title_full | BRAX, Brazilian labeled chest x-ray dataset |
title_fullStr | BRAX, Brazilian labeled chest x-ray dataset |
title_full_unstemmed | BRAX, Brazilian labeled chest x-ray dataset |
title_short | BRAX, Brazilian labeled chest x-ray dataset |
title_sort | brax, brazilian labeled chest x-ray dataset |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9364309/ https://www.ncbi.nlm.nih.gov/pubmed/35948551 http://dx.doi.org/10.1038/s41597-022-01608-8 |
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