ArticleScientific data2022
BRAX, Brazilian labeled chest x-ray dataset.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning.Nature communications · 2026Article
- An explainable biomedical foundation model via large-scale concept-enhanced vision-language pretraining.Nature biomedical engineering · 2026Article
- Foundation models for X-ray interpretation: a narrative review of current techniques and future perspectives in diagnostic imaging.Quantitative imaging in medicine and surgery · 2026Review
- Leveraging Large-Scale Public Data for Artificial Intelligence-Driven Chest X-Ray Analysis and Diagnosis.Diagnostics (Basel, Switzerland) · 2026Article
- PLUTO: a YOLO-based lung field detector for pediatric lateral chest X-rays generalizable to adults.Frontiers in artificial intelligence · 2026Article
- CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray.Medical image analysis · 2025Article
- Chest X-Ray Foundation Model With Global and Local Representations Integration.IEEE transactions on medical imaging · 2025Article
- Pneumonia Detection from Chest X-Ray Images Using Deep Learning and Transfer Learning for Imbalanced Datasets.Journal of imaging informatics in medicine · 2025Article
- IHRAS: Automated Medical Report Generation from Chest X-Rays via Classification, Segmentation, and LLMs.Bioengineering (Basel, Switzerland) · 2025Article
- MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India.BMC medical informatics and decision making · 2025Article
- Article
- A clinically accessible small multimodal radiology model and evaluation metric for chest X-ray findings.Nature communications · 2025Article
- Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm.European radiology · 2024Article
- A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset.Scientific data · 2024Article
- A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation.Scientific data · 2024Article
- Development and External Validation of an Artificial Intelligence-Based Method for Scalable Chest Radiograph Diagnosis: A Multi-Country Cross-Sectional Study.Research (Washington, D.C.) · 2024Article
- Pilot deployment of a cloud-based universal medical image repository in a large public health system: A protocol study.PloS one · 2024Article
- Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning.Biomedical signal processing and control · 2023Article
- Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?Diagnostics (Basel, Switzerland) · 2023Review
- AI-based radiodiagnosis using chest X-rays: A review.Frontiers in big data · 2023Review
Corrections and comments
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Authors and funding
18 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.