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...

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Published in:	Scientific data Vol. 9; no. 1; pp. 487 - 8
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 10-08-2022 Nature Publishing Group Nature Portfolio
Subjects:	692/53/2421 692/699/1785 692/700/1421/1770 Algorithms Brazil Chest Data Descriptor Datasets Humanities and Social Sciences Humans Language multidisciplinary Natural Language Processing Patients Radiography Radiography, Thoracic Radiology Science Science (multidisciplinary) X-Rays Brazil
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Abstract	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. Measurement(s) Chest Radiography Technology Type(s) natural language processing Factor Type(s) radiological findings/labels Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment chest organ Sample Characteristic - Location Brazil
AbstractList	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. 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.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. 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. Measurement(s) Chest Radiography Technology Type(s) natural language processing Factor Type(s) radiological findings/labels Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment chest organ Sample Characteristic - Location Brazil Measurement(s) Chest Radiography Technology Type(s) natural language processing Factor Type(s) radiological findings/labels Sample Characteristic - Organism Homo sapiens Sample Characteristic - Environment chest organ Sample Characteristic - Location Brazil 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.Measurement(s)Chest RadiographyTechnology Type(s)natural language processingFactor Type(s)radiological findings/labelsSample Characteristic - OrganismHomo sapiensSample Characteristic - Environmentchest organSample Characteristic - LocationBrazil
ArticleNumber	487
Author	de Paiva, Joselisa P. Q. Szarf, Gilberto Ribeiro, Guilherme A. S. Haidar Filho, Jorge N. Pollard, Tom Paiva, Victor F. Santos, Paulo V. Beraldo, Gabriel L. Brito, Vanessa M. Bulgarelli, Lucas da Silva, Maria C. B. Amaral, Lucas T. W. Reis, Eduardo P. Lee, Henrique M. H. Celi, Leo A. Amaro, Edson Teles, Gustavo B. S. Johnson, Alistair E. W.
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Cites_doi	10.1006/jbin.2001.1029 10.3389/fninf.2013.00045 10.1148/radiol.2413051535 10.1038/s41746-020-00369-1 10.1016/j.artmed.2021.102075 10.1001/jama.2020.12067 10.1371/journal.pone.0204155 10.1016/j.media.2020.101797 10.1016/S2589-7500(19)30123-2 10.13026/grwk-yh18 10.1016/j.clinimag.2017.11.007 10.1016/j.nic.2020.08.008 10.1016/j.artmed.2020.101878 10.1038/s41597-019-0322-0 10.1609/aaai.v33i01.3301590 10.1161/01.CIR.101.23.e215 10.1109/CVPR.2017.369 10.36416/1806-3756/e20200595
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Snippet	Chest radiographs allow for the meticulous examination of a patient’s chest but demands specialized training for proper interpretation. Automated analysis of... Chest radiographs allow for the meticulous examination of a patient's chest but demands specialized training for proper interpretation. Automated analysis of... Measurement(s) Chest Radiography Technology Type(s) natural language processing Factor Type(s) radiological findings/labels Sample Characteristic - Organism...
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SubjectTerms	692/53/2421 692/699/1785 692/700/1421/1770 Algorithms Brazil Chest Data Descriptor Datasets Humanities and Social Sciences Humans Language multidisciplinary Natural Language Processing Patients Radiography Radiography, Thoracic Radiology Science Science (multidisciplinary) X-Rays
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Title	BRAX, Brazilian labeled chest x-ray dataset
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