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ZhangLabData: Chest X-Ray Dataset

58563774
Tagmedical
Taskclassification
Release YearMade in 2018
LicenseCC BY 4.0
Download1 GB

Introduction #

Released 2018-06-02 ·Daniel Kermany, Kang Zhang, Michael Goldbaum

The authors of the ZhanLabData: Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images addressed challenges related to reliability and interpretability in the implementation of clinical-decision support algorithms for medical imaging. The Chest XRay part has a total of 5,856 patients contributed to the dataset, with 4,273 images characterized as depicting PNEUMONIA_BACTERIA and PNEUMONIA_VIRUS (rest - NORMAL images). They established a diagnostic tool based on a deep-learning framework specifically designed for the screening of patients with common treatable blinding retinal diseases.

The full dataset consists of the following parts:

Authors’ framework employed transfer learning, a technique that involves training a neural network with a fraction of the data used in conventional approaches. This approach was applied to a dataset of optical coherence tomography images, demonstrating performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema.

In addition to achieving high performance, the authors focused on providing a more transparent and interpretable diagnosis. Their approach involved highlighting the regions recognized by the neural network, enhancing the understanding of the decision-making process.

To investigate the generalizability of their AI system, the authors extended the transfer learning framework to the diagnosis of pediatric pneumonia. Highlighting the severity of pneumonia as a leading cause of childhood mortality, particularly in developing countries, they aimed to expedite the diagnosis and referral of these treatable conditions.

The authors emphasized the significance of accurate and timely diagnosis for pediatric pneumonia, a disease responsible for a substantial number of childhood deaths. They utilized chest X-ray images, a standard diagnostic tool, to train their AI system. The dataset comprised chest X-ray images depicting bacterial and viral pneumonia, as well as normal cases. The AI system demonstrated effectiveness in classifying pediatric chest X-rays to detect pneumonia, distinguishing between viral and bacterial pneumonia to facilitate rapid referrals for urgent intervention.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images is a dataset for a classification task. It is used in the medical industry.

The dataset consists of 5856 images with 0 labeled objects. There are 2 splits in the dataset: train (5232 images) and test (624 images). Alternatively, the dataset could be split into 3 classification splits: PNEUMONIA_BACTERIA (2780 images), NORMAL (1583 images), and PNEUMONIA_VIRUS (1493 images). The dataset was released in 2018 by the Zhang Lab, Univercity of San Diego, USA.

Here are the visualized examples for the classes:

Explore #

ZhangLabData: Chest X-Ray dataset has 5856 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
OpenSample annotation mask from ZhangLabData: Chest X-RaySample image from ZhangLabData: Chest X-Ray
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Have a look at 5856 images
View images along with annotations and tags, search and filter by various parameters

License #

Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images is under CC BY 4.0 license.

Source

Citation #

If you make use of the ZhangLabData: Chest X-Ray Images data, please cite the following reference:

Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), 
“Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images”, 
Mendeley Data, V3, doi: 10.17632/rscbjbr9sj.3

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-zhang-lab-data-chest-xray-dataset,
  title = { Visualization Tools for ZhangLabData: Chest X-Ray Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/zhang-lab-data-chest-xray } },
  url = { https://datasetninja.com/zhang-lab-data-chest-xray },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset ZhangLabData: Chest X-Ray can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='ZhangLabData: Chest X-Ray', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

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