VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations
Open accessAbstract
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set.
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“Bounding boxes are commonly used to localise findings on CXRs (Nguyen et al., 2022; Wang et al., 2017; Boecking et al., 2022; Müller et al., 2024) and are easier to annotate than full segmentation masks.”
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- Authors
- Nguyen, H. Q., Lam, K., Le, L. T., Pham, H. H., Tran, D. Q., Nguyen, D. B., Le, D. D., Pham, C. M., Tong, H. T. T., Dinh, D. H., Do, C. D., Doan, L. T., Nguyen, C. N., Nguyen, B. T., Nguyen, Q. V., Hoang, A. D., Phan, H. N., Nguyen, A. T., Ho, P. H., Ngo, D. T., Nguyen, N. T., Nguyen, N. T., Dao, M., & Vu, V.
- Affiliations
- Vingroup (Vietnam) (VN)
- 108 Military Central Hospital (VN)
- Hanoi Medical University Hospital (VN)
- Tam Anh General Hospital, Ho Chi Minh City (VN)
- Yale University (US)
- ORCID
- Venue
- Scientific Data
- Published
- 2022-07-20
- Volume
- 9
- Issue
- 1
- Pages
- 429-429
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC9300612
- OpenAlex
- W3114128166
- Cited by
- 435
- References
- 3
- Citations by year
- 2026: 100 · 2025: 103 · 2024: 100 · 2023: 88 · 2022: 31 · 2021: 12 · 2020: 1
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Data (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- chest radiography, chest radiology, radiologist annotation, lesion localization, anomaly detection, disease classification
- MeSH terms
- Radiologists, Algorithms, Humans, Mass Chest X-Ray, Radiography, Retrospective Studies
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