5 datasets found
  1. P

    ChestX-ray14 Dataset

    • paperswithcode.com
    Updated Nov 13, 2023
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    Xiaosong Wang; Yifan Peng; Le Lu; Zhiyong Lu; Mohammadhadi Bagheri; Ronald M. Summers (2023). ChestX-ray14 Dataset [Dataset]. https://paperswithcode.com/dataset/chestx-ray14
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    Dataset updated
    Nov 13, 2023
    Authors
    Xiaosong Wang; Yifan Peng; Le Lu; Zhiyong Lu; Mohammadhadi Bagheri; Ronald M. Summers
    Description

    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.

  2. NIH Chest X ray 14 (224x224 resized)

    • kaggle.com
    zip
    Updated Jul 8, 2020
    + more versions
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    Khan Fashee Monowar (Sawrup) (2020). NIH Chest X ray 14 (224x224 resized) [Dataset]. https://www.kaggle.com/khanfashee/nih-chest-x-ray-14-224x224-resized
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    zip(2468882507 bytes)Available download formats
    Dataset updated
    Jul 8, 2020
    Authors
    Khan Fashee Monowar (Sawrup)
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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
    
  3. h

    ChestX-Det

    • huggingface.co
    • paperswithcode.com
    • +1more
    Updated Nov 20, 2024
    + more versions
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    Nathaniel Alberti (2024). ChestX-Det [Dataset]. https://huggingface.co/datasets/natealberti/ChestX-Det
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 20, 2024
    Authors
    Nathaniel Alberti
    Description

    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.

  4. n

    NIH Chest X-ray Dataset - Dataset - 國網中心Dataset平台

    • scidm.nchc.org.tw
    Updated Oct 10, 2020
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    (2020). NIH Chest X-ray Dataset - Dataset - 國網中心Dataset平台 [Dataset]. https://scidm.nchc.org.tw/dataset/nih-chest-x-ray-dataset
    Explore at:
    Dataset updated
    Oct 10, 2020
    Description

    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.)

  5. NIH CLAHE Enhanced Chest X-rays

    • kaggle.com
    Updated Jan 19, 2025
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    Rahul Goel (2025). NIH CLAHE Enhanced Chest X-rays [Dataset]. https://www.kaggle.com/datasets/rahulogoel/clahe-enhancement-on-chestx-ray14
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 19, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Rahul Goel
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Contrast-limited adaptive histogram equalization (CLAHE) applied on NIH Chest X-rays Dataset. Code

    Without CLAHEWith CLAHE
    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F21948533%2F36180a46f11e77988905ebeed0321084%2F00000001_000.png?generation=1737239754249503&alt=media" alt="">https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F21948533%2F410b2cdcb14439d27e00226576be16c0%2F00000001_000.png?generation=1737239772316485&alt=media" alt="">

    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.

    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

    File contents

    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:

    • Image Index: File name
    • Finding Labels: Disease type (Class label)
    • Follow-up #
    • Patient ID
    • Patient Age
    • Patient Gender
    • View Position: X-ray orientation
    • OriginalImage (Width)
    • OriginalImage (Height)
    • OriginalImagePixelSpacing (x)
    • OriginalImagePixelSpacing (y)

    labels.csv: Contains one-hot encoded format for labels.

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Xiaosong Wang; Yifan Peng; Le Lu; Zhiyong Lu; Mohammadhadi Bagheri; Ronald M. Summers (2023). ChestX-ray14 Dataset [Dataset]. https://paperswithcode.com/dataset/chestx-ray14

ChestX-ray14 Dataset

Explore at:
Dataset updated
Nov 13, 2023
Authors
Xiaosong Wang; Yifan Peng; Le Lu; Zhiyong Lu; Mohammadhadi Bagheri; Ronald M. Summers
Description

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.

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