MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports

Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient’s chest, but requires specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest...

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Bibliographic Details
Published in:Scientific data 2019-12, Vol.6 (1), p.317-8, Article 317
Main Authors: Johnson, Alistair E. W., Pollard, Tom J., Berkowitz, Seth J., Greenbaum, Nathaniel R., Lungren, Matthew P., Deng, Chih-ying, Mark, Roger G., Horng, Steven
Format: Article
Language:English
Subjects:
692/308/575
692/700/1421/1770
Algorithms
Computer vision
Data Descriptor
Data Mining
Databases, Factual
Datasets
Digital curation
Humanities and Social Sciences
Humans
Image Interpretation, Computer-Assisted
multidisciplinary
Natural Language Processing
Radiography
Radiography, Thoracic
Radiology
Science
Science (multidisciplinary)
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Summary:Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient’s chest, but requires specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. Here we describe MIMIC-CXR, a large dataset of 227,835 imaging studies for 65,379 patients presenting to the Beth Israel Deaconess Medical Center Emergency Department between 2011–2016. Each imaging study can contain one or more images, usually a frontal view and a lateral view. A total of 377,110 images are available in the dataset. Studies are made available with a semi-structured free-text radiology report that describes the radiological findings of the images, written by a practicing radiologist contemporaneously during routine clinical care. All images and reports have been de-identified to protect patient privacy. The dataset is made freely available to facilitate and encourage a wide range of research in computer vision, natural language processing, and clinical data mining. Measurement(s) Radiograph • investigation results report Technology Type(s) Chest Radiography • digital curation • Radiologist Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.10303823
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-019-0322-0