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MIMIC-CXR-JPG - chest radiographs with structured labels
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The MIMIC Chest X-ray JPG (MIMIC-CXR-JPG) Database v2.0.0 is a large publicly available dataset of chest radiographs in JPG format with structured labels derived from free-text radiology reports. The MIMIC-CXR-JPG dataset is wholly derived from [MIMIC-CXR](https://physionet.org/content/mimic-cxr/), providing JPG format files derived from the DICOM images and structured labels derived from the free-text reports. The aim of MIMIC-CXR-JPG is to provide a convenient processed version of MIMIC-CXR, as well as to provide a standard reference for data splits and image labels. The dataset contains 377,110 JPG format images and structured labels derived from the 227,827 free-text radiology reports associated with these images. The dataset is de-identified to satisfy the US Health Insurance Portability and Accountability Act of 1996 (HIPAA) Safe Harbor requirements. Protected health information (PHI) has been removed. The dataset is intended to support a wide body of research in medicine including image understanding, natural language processing, and decision support.
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The MIMIC-MM dataset is composed by joining MIMIC-IV, MIMIC-CXR, and MIMIC-IV-Note datasets.
“The model is trained and tested with the MIMIC-MM dataset, which is composed by joining MIMIC-IV, MIMIC-CXR, and MIMIC-IV-Note datasets [5], [6], [7], [8].”
From Multimodal risk prediction with physiological signals, medical images and clinical notes (2024) · cites this paper as [6]
The Abstract (page not specified) identifies MIMIC-CXR-JPG as a dataset wholly derived from MIMIC-CXR, supporting the citation’s connection to the chest-radiograph component. It does not describe MIMIC-MM or say that MIMIC-CXR was joined with MIMIC-IV and MIMIC-IV-Note. (LLM, conf=0.84)
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- Ray Smith. An overview of the tesseract OCR engine. In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), volume 2, pages 629–633. IEEE, 2007.
- Farah S Ali, Samantha G Harrington, Stephen B Kennedy, and Sarwat Hussain. Diagnostic radiology in Liberia: a country report. Journal of Global Radiology, 1(2):6, 2015.
- David A Rosman, Jean Jacques Nshizirungu, Emmanuel Rudakemwa, Crispin Moshi, Jean de Dieu Tuyisenge,Etienne Uwimana, and Louise Kalisa. Imaging in the land of 1000 hills: Rwanda radiology country report. Journal of Global Radiology, 1(1):5, 2015.
- Sarah Bastawrous and Benjamin Carney. Improving patient safety: Avoiding unread imaging exams in the nationalva enterprise electronic health record. Journal of digital imaging, 30(3):309–313, 2017.
- Abi Rimmer. Radiologist shortage leaves patient care at risk, warns royal college. BMJ: British Medical Journal(Online), 359, 2017.
- Andrew B Rosenkrantz, Wenyi Wang, Danny R Hughes, and Richard Duszak Jr. A county-level analysis of theus radiologist workforce: physician supply and subspecialty characteristics. Journal of the American College of Radiology, 15(4):601–606, 2018.
- Andrew B Rosenkrantz, Danny R Hughes, and Richard Duszak Jr. The US radiologist workforce: an analysis of temporal and geographic variation by using large national datasets. Radiology, 279(1):175–184, 2015.
- Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund,Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al. CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In Thirty-Third AAAI Conference on Artificial Intelligence, 2019.
- Peng Y, Wang X, Lu L, Bagheri M, Summers R, Lu Z. NegBio: a high-performance tool for negation and uncertainty detection in radiology reports. AMIA Summits on Translational Science Proceedings. 2018;2018:188.
- Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. InProceedings of the IEEE conference on computer vision and pattern recognition 2017 (pp. 2097-2106).
- Simon, M., Leeming, B. W., Bleich, H. L., Reiffen, B., Byrd, J., Blair, D., & Shimm, D. (1974). Computerized radiology reporting using coded language. Radiology, 113(2), 343-349.
- Predicting Findings from Radiology Indication Text: BERT vs Naïve Bayes Classification Anthony F. Peressini. Journal of Imaging Informatics in Medicine, 2026.
- CXR-CoRe: Two-Stage View- and Section-Aware Study-Level Chest X-ray Report Retrieval Yan Tian, Jianbing Ma. Journal of Imaging Informatics in Medicine, 2026.
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Peressini, A. F. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02404-1
Wang, L., Zhu, J., Zhou, R., Zi, H., Zhong, C., Zhou, H., Vereshchako, M., Wu, Z., Song, J., Zhou, Q., et al. (2026) · medRxiv · doi:10.64898/2026.10.23.26365792
Zemzem, W., Aouadni, I., & Kacem, M. H. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.09.412
Khairnar, N. S., & Sane, S. S. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100519
Metadata
- Authors
- Johnson, A., Lungren, M., Peng, Y., Lu, Z., Mark, R., Berkowitz, S., & Horng, S.
- Affiliations
- Massachusetts Institute of Technology (US)
- Stanford University School of Medicine
- National Institutes of Health (US)
- Beth Israel Deaconess Medical Center (US)
- Venue
- PhysioNet
- Publisher
- PhysioNet
- Published
- 2024
- OpenAlex
- W6941497511
- Cited by
- 46
- References
- 0
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 10
- Type
- dataset
- Language
- en
- Open access
- green
- doi.org
- Locations
- PhysioNet
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- chest radiograph, radiology reports, natural language processing, medical image interpretation, clinical decision support, MIMIC-CXR, data splits
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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