ChestX-ray14 is a medical imaging dataset which comprises 112,120 frontal-view X-ray images of 30,805 (collected from the year of 1992 to 2015) unique patients with the text-mined fourteen common disease labels, mined from the text radiological reports via NLP techniques. It expands on ChestX-ray8 by adding six additional thorax diseases: Edema, Emphysema, Fibrosis, Pleural Thickening and Hernia.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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.)
The image labels are NLP extracted so there could be some erroneous labels but the NLP labeling accuracy is estimated to be >90%.
Very limited numbers of disease region bounding boxes (See BBoxlist2017.csv)
Chest x-ray radiology reports are not anticipated to be publicly shared. Parties who use this public dataset are encouraged to share their “updated” image labels and/or new bounding boxes in their own studied later, maybe through manual annotation
Image format: 112,120 total images with size 1024 x 1024
images_001.zip: Contains 4999 images
images_002.zip: Contains 10,000 images
images_003.zip: Contains 10,000 images
images_004.zip: Contains 10,000 images
images_005.zip: Contains 10,000 images
images_006.zip: Contains 10,000 images
images_007.zip: Contains 10,000 images
images_008.zip: Contains 10,000 images
images_009.zip: Contains 10,000 images
images_010.zip: Contains 10,000 images
images_011.zip: Contains 10,000 images
images_012.zip: Contains 7,121 images
README_ChestXray.pdf: Original README file
BBoxlist2017.csv: Bounding box coordinates. Note: Start at x,y, extend horizontally w pixels, and vertically h pixels
Image Index: File name
Finding Label: Disease type (Class label)
Bbox x
Bbox y
Bbox w
Bbox h
Dataentry2017.csv: Class labels and patient data for the entire dataset
Image Index: File name
Finding Labels: Disease type (Class label)
Follow-up #
Patient ID
Patient Age
Patient Gender
View Position: X-ray orientation
OriginalImageWidth
OriginalImageHeight
OriginalImagePixelSpacing_x
OriginalImagePixelSpacing_y
There are 15 classes (14 diseases, and one for "No findings"). Images can be classified as "No findings" or one or more disease classes:
Atelectasis
Consolidation
Infiltration
Pneumothorax
Edema
Emphysema
Fibrosis
Effusion
Pneumonia
Pleural_thickening
Cardiomegaly
Nodule Mass
Hernia
There are 12 zip files in total and range from ~2 gb to 4 gb in size. Additionally, we randomly sampled 5% of these images and created a smaller dataset for use in Kernels. The random sample contains 5606 X-ray images and class labels.
Sample: sample.zip
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-ray8Hospital-ScaleChestCVPR2017_paper.pdf
NIH News release: NIH Clinical Center provides one of the largest publicly available chest x-ray datasets to scientific community
Original source files and documents: https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/36938765345
ChestX-Det is a chest X-Ray dataset with instance-level annotations (boxes and masks). ChestX-Det is a subset of the public dataset NIH ChestX-ray14. It contains ~3500 images of 13 common disease categories labeled by three board-certified radiologists. I created segmentation masks for each image in the dataset. Each image is mapped to a unique RGB value. The repository from Deepwise AILab can be found at: https://github.com/Deepwise-AILab/ChestX-Det-Dataset. More information at:… See the full description on the dataset page: https://huggingface.co/datasets/natealberti/ChestX-Det.
https://www.kaggle.com/nih-chest-xrays 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.)
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Contrast-limited adaptive histogram equalization (CLAHE) applied on NIH Chest X-rays Dataset. Code
Without CLAHE | With CLAHE |
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The tile size and clip limit are critical hyper-parameters for this method. An incorrect choosing of hyper-parameters could have a big influence on the image quality. The optimal ones (tileGridSize (10, 10) and the clip limit (3) are chosen here.
There are 15 classes (14 diseases, and one for "No findings"). Images can be classified as "No findings" or one or more disease classes:
IMAGE: Zip file containing over 112k 1024x1024 CLAHE Enhanced Chest X-ray images.
BBox_list.csv: Bounding box coordinates. Start at x, y, extend horizontally w pixels, and vertically h pixels: - Image Index: File name - Finding Label: Disease type (Class label) - Bbox x - Bbox y - Bbox w - Bbox h
Data_entry.csv: Class labels and patient data for the entire dataset:
labels.csv: Contains one-hot encoded format for labels.
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ChestX-ray14 is a medical imaging dataset which comprises 112,120 frontal-view X-ray images of 30,805 (collected from the year of 1992 to 2015) unique patients with the text-mined fourteen common disease labels, mined from the text radiological reports via NLP techniques. It expands on ChestX-ray8 by adding six additional thorax diseases: Edema, Emphysema, Fibrosis, Pleural Thickening and Hernia.