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Chest X-ray exams are one of the most frequent and cost-effective medical imaging examinations available. However, clinical diagnosis of a chest X-ray can be challenging and sometimes more difficult than diagnosis via chest CT imaging. The lack of large publicly available datasets with annotations means it is still very difficult, if not impossible, to achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites with chest X-rays. One major hurdle in creating large X-ray image datasets is the lack resources for labeling so many images. Prior to the release of this dataset, Openi was the largest publicly available source of chest X-ray images with 4,143 images available.
This NIH Chest X-ray Dataset is comprised of 112,120 X-ray images with disease labels from 30,805 unique patients. To create these labels, the authors used Natural Language Processing to text-mine disease classifications from the associated radiological reports. The labels are expected to be >90% accurate and suitable for weakly-supervised learning. The original radiology reports are not publicly available but you can find more details on the labeling process in this Open Access paper: "ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases." (Wang et al.)
sample.zip: Contains 5,606 images with size 1024 x 1024
sample_labels.csv: Class labels and patient data for the entire dataset
There are 15 classes (14 diseases, and one for "No findings") in the full dataset, but since this is drastically reduced version of the full dataset, some of the classes are sparse with the labeled as "No findings"
The full dataset can be found here. There are 12 zip files in total and range from ~2 gb to 4 gb in size.
Original TAR archives were converted to ZIP archives to be compatible with the Kaggle platform
CSV headers slightly modified to be more explicit in comma separation and also to allow fields to be self-explanatory
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. IEEE CVPR 2017, ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.pdf
Original source files and documents: https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345
This work was supported by the Intramural Research Program of the NClinical Center (clinicalcenter.nih.gov) and National Library of Medicine (www.nlm.nih.gov).