The NIH Chest X-ray dataset consists of 100,000 de-identified images of chest x-rays. The images are in PNG format.

The data is provided by the NIH Clinical Center and is available through the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC

National Institutes of Health Chest X-Ray Dataset

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.)

Data limitations:

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

File contents

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

Class descriptions

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

Full Dataset Content

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

Modifications to original data

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

Citations

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

![]() (1, Atelectasis; 2, Cardiomegaly; 3, Effusion; 4, Infiltration; 5, Mass; 6, Nodule; 7, Pneumonia; 8, Pneumothorax; 9, Consolidation; 10, Edema; 11, Emphysema; 12, Fibrosis; 13, Pleural_Thickening; 14 Hernia) ### Background & Motivation: Chest X-ray exam is one of the most frequent and cost-effective medical imaging examination. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past, and especially in recent deep learning work on Tuberculosis (TB) classification. To achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult, if not impossible when only several thousands of images are employed for study. This is evident from [2] where the performance deep neural networks for thorax disease recognition is severely

BahaaEldin0/NIH-Chest-Xray-14 dataset hosted on Hugging Face and contributed by the HF Datasets community

Performance of the models on the NIH ChestX-ray14 external dataset.

Dataset

This dataset was created by Hani MO

Contents

This dataset is resized versions of images to 224x224. ![]() (1, Atelectasis; 2, Cardiomegaly; 3, Effusion; 4, Infiltration; 5, Mass; 6, Nodule; 7, Pneumonia; 8, Pneumothorax; 9, Consolidation; 10, Edema; 11, Emphysema; 12, Fibrosis; 13, Pleural_Thickening; 14 Hernia) ### Background & Motivation: Chest X-ray exam is one of the most frequent and cost-effective medical imaging examination. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past, and especially in recent deep learning work on Tuberculosis (TB) classification. To achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult, if not impossible when only several thousands of images are employed for study. This is evident from [2] where the performance deep neu

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.)

NIH Chest X-ray Reasoning Dataset (Subset + GPT-4.1 Mini Outputs)

This repository contains a curated subset of the NIH ChestX-ray14 dataset and corresponding reasoning outputs generated using the OpenAI GPT-4.1 Mini API.

  📦 Contents

nih-dataset-subset_generation_16k.ipynb: Jupyter notebook used to create a balanced 16k subset from the full NIH ChestX-ray14 dataset
nih_balanced_filtered_16K.csv: CSV file containing metadata for the 16,000-image balanced subset… See the full description on the dataset page: https://huggingface.co/datasets/Manusinhh/nih-subset-with-reasoning-generated.

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.

The database comprises over 72,000 chest X-ray images collected from multiple sources, including NIH ChestX-ray14, COVIDx CXR-4, Shenzhen Chest X-ray Set, Montgomery County X-ray Set, Chest X-Ray Images (Pneumonia), and TB Portal. The images are classified into four primary classes: Normal, Tuberculosis (TB), COVID-19, and Pneumonia, covering a wide range of thoracic diseases. The dataset includes images in formats such as PNG, JPG, and DICOM, sourced from diverse clinical settings. It is a valuable resource for research and development in medical imaging, particularly for disease detection and classification tasks.

Normal: 18,097 images Covid-19: 18,011 images Pneumonia: 18,187 images Tuberculosis: 18,003 images

All datasets are publicly available . Researchers and developers are encouraged to review the licensing and usage terms for each dataset before downloading and using the images.

This repository is a valuable resource for advancing the field of medical imaging and improving diagnostic accuracy for thoracic diseases. Let me know if you need further assistance or additional datasets!

BackgroundPneumothorax can precipitate a life-threatening emergency due to lung collapse and respiratory or circulatory distress. Pneumothorax is typically detected on chest X-ray; however, treatment is reliant on timely review of radiographs. Since current imaging volumes may result in long worklists of radiographs awaiting review, an automated method of prioritizing X-rays with pneumothorax may reduce time to treatment. Our objective was to create a large human-annotated dataset of chest X-rays containing pneumothorax and to train deep convolutional networks to screen for potentially emergent moderate or large pneumothorax at the time of image acquisition.Methods and findingsIn all, 13,292 frontal chest X-rays (3,107 with pneumothorax) were visually annotated by radiologists. This dataset was used to train and evaluate multiple network architectures. Images showing large- or moderate-sized pneumothorax were considered positive, and those with trace or no pneumothorax were considered negative. Images showing small pneumothorax were excluded from training. Using an internal validation set (n = 1,993), we selected the 2 top-performing models; these models were then evaluated on a held-out internal test set based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV). The final internal test was performed initially on a subset with small pneumothorax excluded (as in training; n = 1,701), then on the full test set (n = 1,990), with small pneumothorax included as positive. External evaluation was performed using the National Institutes of Health (NIH) ChestX-ray14 set, a public dataset labeled for chest pathology based on text reports. All images labeled with pneumothorax were considered positive, because the NIH set does not classify pneumothorax by size. In internal testing, our “high sensitivity model” produced a sensitivity of 0.84 (95% CI 0.78–0.90), specificity of 0.90 (95% CI 0.89–0.92), and AUC of 0.94 for the test subset with small pneumothorax excluded. Our “high specificity model” showed sensitivity of 0.80 (95% CI 0.72–0.86), specificity of 0.97 (95% CI 0.96–0.98), and AUC of 0.96 for this set. PPVs were 0.45 (95% CI 0.39–0.51) and 0.71 (95% CI 0.63–0.77), respectively. Internal testing on the full set showed expected decreased performance (sensitivity 0.55, specificity 0.90, and AUC 0.82 for high sensitivity model and sensitivity 0.45, specificity 0.97, and AUC 0.86 for high specificity model). External testing using the NIH dataset showed some further performance decline (sensitivity 0.28–0.49, specificity 0.85–0.97, and AUC 0.75 for both). Due to labeling differences between internal and external datasets, these findings represent a preliminary step towards external validation.ConclusionsWe trained automated classifiers to detect moderate and large pneumothorax in frontal chest X-rays at high levels of performance on held-out test data. These models may provide a high specificity screening solution to detect moderate or large pneumothorax on images collected when human review might be delayed, such as overnight. They are not intended for unsupervised diagnosis of all pneumothoraces, as many small pneumothoraces (and some larger ones) are not detected by the algorithm. Implementation studies are warranted to develop appropriate, effective clinician alerts for the potentially critical finding of pneumothorax, and to assess their impact on reducing time to treatment.

The provided Chest Xray Masks and Labels dataset includes X-rays along with their corresponding masks. Notably, some masks may be absent, so cross-referencing images and masks is recommended. This dataset is derived from a modification of an original dataset, which combines Shenzhen and Montgomery County publicly available chest X-ray datasets.