The MIMIC Chest X-ray (MIMIC-CXR) Database v2.0.0 is a large publicly available dataset of chest radiographs in DICOM format with free-text radiology reports. The dataset contains 377,110 images corresponding to 227,835 radiographic studies performed at the Beth Israel Deaconess Medical Center in Boston, MA. The dataset is de-identified to satisfy the US Health Insurance Portability and Accountability Act of 1996 (HIPAA) Safe Harbor requirements. Protected health information (PHI) has been removed. The dataset is intended to support a wide body of research in medicine including image understanding, natural language processing, and decision support.

Doha

🦵 Knee X-ray Osteoporosis Database

Knee X-ray Images Paired with Clinical Factors for Accessible Osteoporosis Screening

The Knee X-ray Osteoporosis Database aims to support research in developing accessible, cost-effective systems for early detection of osteoporosis using knee X-ray images and associated clinical data.

📚 Background

Osteoporosis is a common bone disease affecting millions worldwide. Although Dual Energy X-ray Absorptiometry (DXA) is the standard diagnostic technique, its high cost and limited availability drive the need for alternative, affordable detection methods.

This dataset promotes data-driven research by providing a well-structured collection of knee X-ray images and clinical indicators, enabling the development of AI-powered diagnostic tools.

📦 Dataset Contents

The database includes:

• 🖼️ Knee X-ray Images
  High-quality X-ray images of knee joints from multiple participants.

• 📋 Clinical Factors
  Tabular data including:

  • Age
  • Gender
  • Menopause age (if applicable)
  • Comorbidities (e.g., diabetes, thyroid issues)
  • Fracture history
  • Lifestyle habits (e.g., diet, physical activity)

• 📊 T-score Values
  Reference T-score values obtained via a Quantitative Ultrasound System for each participant, serving as osteoporosis diagnosis markers.

💡 Use Cases

This dataset is ideal for:

• Training and evaluating machine learning/deep learning models for osteoporosis detection from X-rays.
• Analyzing relationships between clinical factors and bone health indicators.
• Designing affordable pre-screening tools based on image analysis.
• Validating prediction models using ultrasound-based T-score references.

🏷️ Tags

Medical Imaging · Knee X-rays · Osteoporosis · Deep Learning · Clinical Data

📜 License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

✅ You are free to use, share, and adapt the dataset—even for commercial purposes—as long as proper credit is given.

✍️ Citation

If you use this dataset in your work, please cite it as:

Insha Majeed Wani, Sakshi Arora (2021). Knee X-ray Osteoporosis Database. Mendeley Data, V2. https://doi.org/10.17632/fxjm8fb6mw.2

🏫 Affiliation

Shri Mata Vaishno Devi University
🇮🇳 India

The CheXmask Database presents a comprehensive, uniformly annotated collection of chest radiographs, constructed from five public databases: ChestX-ray8, Chexpert, MIMIC-CXR-JPG, Padchest and VinDr-CXR. The database aggregates 657,566 anatomical segmentation masks derived from images which have been processed using the HybridGNet model to ensure consistent, high-quality segmentation. To confirm the quality of the segmentations, we include in this database individual Reverse Classification Accuracy (RCA) scores for each of the segmentation masks. This dataset is intended to catalyze further innovation and refinement in the field of semantic chest X-ray analysis, offering a significant resource for researchers in the medical imaging domain.

A SQLite database containing mass absorption coefficient (both discrete and continuous), atomic sub-shell binding energy, X-ray energy, jump ratio, ground-state occupancy, atomic relaxation rate following core shell ionization and X-ray linewidth data. The data is in the common SQLite format and also available in SQL format. SQLite is an open-source database which is supported on many different platforms. This database represents a compilation of data from other sources. Each datum is labeled with a literature reference which represents the source. The references are listed in the LIT_REFERENCES table with associated BIBTEX reference data. The two exceptions to this rule are the FFAST and FFAST_EXTRA tables which are associated with the Chantler2005 reference.

A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors have created a database of chest X-ray images for COVID-19 positive cases along with Normal and Viral Pneumonia images. This COVID-19, normal, and other lung infection dataset is released in stages. In the first release, we have released 219 COVID-19, 1341 normal, and 1345 viral pneumonia chest X-ray (CXR) images. In the first update, we have increased the COVID-19 class to 1200 CXR images. In the 2nd update, we have increased the database to 3616 COVID-19 positive cases along with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images and corresponding lung masks. We will continue to update this database as soon as we have new x-ray images for COVID-19 pneumonia patients.

Background

The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.

Purpose

To create the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. The Radiological Society of North America (RSNA) assembled the RSNA International COVID-19 Open Radiology Database (RICORD) collection of COVID-related imaging datasets and expert annotations to support research and education. RICORD data will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Materials and Methods

This dataset was created through a collaboration between the RSNA and Society of Thoracic Radiology (STR). Clinical annotation by thoracic radiology subspecialists was performed for all COVID positive chest radiography (CXR) imaging studies using a labeling schema based upon guidelines for reporting classification of COVID-19 findings in CXRs (see Review of Chest Radiograph Findings of COVID-19 Pneumonia and Suggested Reporting Language, Journal of Thoracic Imaging).

Results

The RSNA International COVID-19 Open Annotated Radiology Database (RICORD) consists of 998 chest x-rays from 361 patients at four international sites annotated with diagnostic labels.

Patient Selection: Patients at least 18 years in age receiving positive diagnosis for COVID-19.

Data Abstract

1. 998 Chest x-ray examinations from 361 patients.

2. Annotations with labels:

   1. Classification

      • Typical Appearance
        Multifocal bilateral, peripheral opacities, and/or Opacities with rounded morphology
        Lower lung-predominant distribution (Required Feature - must be present with either or both of the first two opacity patterns)

      • Indeterminate Appearance
        Absence of typical findings AND Unilateral, central or upper lung predominant distribution of airspace disease

      • Atypical Appearance
        Pneumothorax or pleural effusion, Pulmonary Edema, Lobar Consolidation, Solitary lung nodule or mass, Diffuse tiny nodules, Cavity

      • Negative for Pneumonia
        No lung opacities

   2. Airspace Disease Grading
      Lungs are divided on frontal chest xray into 3 zones per lung (6 zones total). The upper zone extends from the apices to the superior hilum. The mid zone spans between the superior and inferior hilar margins. The lower zone extends from the inferior hilar margins to the costophrenic sulci.

      • Mild - Required if not negative for pneumonia
        Opacities in 1-2 lung zones

      • Moderate - Required if not negative for pneumonia
        Opacities in 3-4 lung zones

      • Severe - Required if not negative for pneumonia
        Opacities in >4 lung zones

3. Supporting clinical variables: MRN*, Age, Study Date*, Exam Description, Sex, Study UID*, Image Count, Modality, Testing Result, Specimen Source (* pseudonymous values).

Research Benefits

RICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical “ground truth” as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.

The MIMIC Chest X-ray JPG (MIMIC-CXR-JPG) Database v2.0.0 is a large publicly available dataset of chest radiographs in JPG format with structured labels derived from free-text radiology reports. The MIMIC-CXR-JPG dataset is wholly derived from MIMIC-CXR, providing JPG format files derived from the DICOM images and structured labels derived from the free-text reports. The aim of MIMIC-CXR-JPG is to provide a convenient processed version of MIMIC-CXR, as well as to provide a standard reference for data splits and image labels. The dataset contains 377,110 JPG format images and structured labels derived from the 227,827 free-text radiology reports associated with these images. The dataset is de-identified to satisfy the US Health Insurance Portability and Accountability Act of 1996 (HIPAA) Safe Harbor requirements. Protected health information (PHI) has been removed. The dataset is intended to support a wide body of research in medicine including image understanding, natural language processing, and decision support.

Authors of the Dataset:

Pratik Bhowal (B.E., Dept of Electronics and Instrumentation Engineering, Jadavpur University Kolkata, India) [LinkedIn], [Github] Subhankar Sen (B.Tech, Dept of Computer Science Engineering, Manipal University Jaipur, India) [LinkedIn], [Github], [Google Scholar] Jin Hee Yoon (faculty of the Dept. of Mathematics and Statistics at Sejong University, Seoul, South Korea) [LinkedIn], [Google Scholar] Zong Woo Geem (faculty of College of IT Convergence at Gachon University, South Korea) [LinkedIn], [Google Scholar] Ram Sarkar( Professor at Dept. of Computer Science Engineering, Jadavpur Univeristy Kolkata, India) [LinkedIn], [Google Scholar]

Overview The authors have created a new dataset known as Novel COVID-19 Chestxray Repository by the fusion of publicly available chest-xray image repositories. In creating this combined dataset, three different datasets obtained from the Github and Kaggle databases,created by the authors of other research studies in this field, were utilized.In our study,frontal and lateral chest X-ray images are used since this view of radiography is widely used by radiologist in clinical diagnosis.In the following section, authors have summarized how this dataset is created.

COVID-19 Radiography Database: The first release of this dataset reports 219 COVID-19,1345 viral pneumonia and 1341 normal radiographic chest X-ray images. This dataset was created by a team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh in collaboration with medical doctors and specialists from Pakistan and Malaysia.This database is regularly updated with the emergence of new cases of COVID-19 patients worldwide.Related Paper:https://arxiv.org/abs/2003.13145

COVID-Chestxray set:Joseph Paul Cohen and Paul Morrison and Lan Dao have created a public image repository on Github which consists both CT scans and digital chest x-rays.The data was collected mainly from retrospective cohorts of pediatric patients from Guangzhou Women and Children’s medical center.With the aid of metadata information provided along with the dataset,we were able to extract 521 COVID-19 positive,239 viral and bacterial pneumonias;which are of the following three broad categories:Middle East Respiratory Syndrome (MERS),Severe Acute Respiratory Syndrome (SARS), and Acute Respiratory Distress syndrome (ARDS);and 218 normal radiographic chest X-ray images of varying image resolutions. Related Paper: https://arxiv.org/abs/2006.11988

Actualmed COVID chestxray dataset:Actualmed-COVID-chestxray-dataset comprises of 12 COVID-19 positive and 80 normal radiographic chest x-ray images.

The combined dataset includes chest X-ray images of COVID-19,Pneumonia and Normal (healthy) classes, with a total of 752, 1584, and 1639 images respectively. Information about the Novel COVID-19 Chestxray Database and its parent image repositories is provided in Table 1.

Table 1: Dataset Description | Dataset| COVID-19 |Pneumonia | Normal | | ------------- | ------------- | ------------- | -------------| | COVID Chestxray set | 521 |239|218| | COVID-19 Radiography Database(first release) | 219 |1345|1341| | Actualmed COVID chestxray dataset| 12 |0|80| | Total|752|1584|1639|

DATA ACCESS AND USE: Academic/Non-Commercial Use Dataset License : Database: Open Database, Contents: Database Contents

NIH Chest X-ray Dataset

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.)

Link to paper

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 BBox_list_2017.csv)

File contents

• Image format: 880 total images with size 1024 x 1024
• bbox_img: Contains 880 bbox images
• README_ChestXray.pdf: Original README file
• BBox_list_2017.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
• Data_entry_2017.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
• label.csv: Class labels
• tesnorlfow.csv: tensorflow version of the dataset

Class descriptions

There are 8 classes . Images can be classified as one or more disease classes: - Infiltrate - Atelectasis - Pneumonia - Cardiomegaly - Effusion - Pneumothorax - Mass - Nodule

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-ray8_Hospital-Scale_Chest_CVPR_2017_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

Acknowledgements

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).

This X-ray transition table provides the energies and wavelengths for the K and L transitions connecting energy levels having principal quantum numbers n = 1, 2, 3, and 4. The elements covered include Z = 10, neon to Z = 100, fermium. There are two unique features of this database: (1) all experimental values are on a scale consistent with the International System of measurement (the SI) and the numerical values are determined using constants from the Recommended Values of the Fundamental Physical Constants: 1998 [115] and (2) accurate theoretical estimates are included for all transitions. Version 1.2

Both healthy and tuberculosis images are from "Tuberculosis (TB) Chest X-Ray Database'' collected by researchers from Qatar and Dhaka University, Doha, Qatar, and collaboration with doctors from Hamad Medical Corporation and Bangladesh. All the images are CRX in PNG format and a size 512x512.

The COVID and Pneumonia images are also from a Kaggle dataset: "COVID-19 Radiography Database'' , which collects images from different sources. This dataset was collected by the same researchers as the "Tuberculosis (TB) Chest X-Ray Database''. All the images in this database are CRX in PNG format and a size 256X256.

X-rays result of patients with pneumonia and normal chest radiographs. X-rays were taken in frontal and lateral projections using Apollo DRF (VILLA SISTEMI MEDICALI).

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.)

The XRAY database table contains selected parameters from almost all HEASARC X-ray catalogs that have source positions located to better than a few arcminutes. The XRAY database table was created by copying all of the entries and common parameters from the tables listed in the Component Tables section. The XRAY database table has many entries but relatively few parameters; it provides users with general information about X-ray sources, obtained from a variety of catalogs. XRAY is especially suitable for cone searches and cross-correlations with other databases. Each entry in XRAY has a parameter called 'database_table' which indicates from which original database the entry was copied; users can browse that original table should they wish to examine all of the parameter fields for a particular entry. For some entries in XRAY, some of the parameter fields may be blank (or have zero values); this indicates that the original database table did not contain that particular parameter or that it had this same value there. The HEASARC in certain instances has included X-ray sources for which the quoted value for the specified band is an upper limit rather than a detection. The HEASARC recommends that the user should always check the original tables to get the complete information about the properties of the sources listed in the XRAY master source list. This master catalog is updated periodically whenever one of the component database tables is modified or a new component database table is added. This is a service provided by NASA HEASARC .

NIST X-ray Photoelectron Spectroscopy Database XPS contains over 33,000 data records that can be used for the identification of unknown lines, retrieval of data for selected elements (binding energy, Auger kinetic energy, chemical shift, and surface or interface core-level shift), retrieval of data for selected compounds (according to chemical name, selected groups of elements, or chemical classes), display of Wagner plots, and retrieval of data by scientific citation. For the newer data records, additional information is provided on the specimen material, the conditions of measurement, and the analysis of the data. Version 5.0 includes the addition of Digital Object Identifiers (DOI) to each of the citations. Additionally, Version 5.0 has new features including chemical shift plots, custom-built components for displaying both formatted molecular formulas and formatted spectral lines, and spectral sorting functions of photoelectron lines and Auger Parameters.

The XRAY database table contains selected parameters from almost all HEASARC X-ray catalogs that have source positions located to better than a few arcminutes. The XRAY database table was created by copying all of the entries and common parameters from the tables listed in the Component Tables section. The XRAY database table has many entries but relatively few parameters; it provides users with general information about X-ray sources, obtained from a variety of catalogs. XRAY is especially suitable for cone searches and cross-correlations with other databases. Each entry in XRAY has a parameter called 'database_table' which indicates from which original database the entry was copied; users can browse that original table should they wish to examine all of the parameter fields for a particular entry. For some entries in XRAY, some of the parameter fields may be blank (or have zero values); this indicates that the original database table did not contain that particular parameter or that it had this same value there. The HEASARC in certain instances has included X-ray sources for which the quoted value for the specified band is an upper limit rather than a detection. The HEASARC recommends that the user should always check the original tables to get the complete information about the properties of the sources listed in the XRAY master source list. This master catalog is updated periodically whenever one of the component database tables is modified or a new component database table is added. This is a service provided by NASA HEASARC .

************Tuberculosis (TB) Chest X-ray Database************ A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Malaysia in collaboration with medical doctors from Hamad Medical Corporation and Bangladesh have created a database of chest X-ray images for Tuberculosis (TB) positive cases along with Normal images. In our current release, there are 700 TB images publicly accessible and 2800 TB images can be downloaded from NIAID TB portal[3] by signing an agreement, and 3500 normal images.

Note: -The research team managed to classify TB and Normal Chest X-ray images with an accuracy of 98.3%. This scholarly work is published in IEEE Access. Please make sure you give credit to us while using the dataset, code, and trained models.

Credit should go to the following: Tawsifur Rahman, Amith Khandakar, Muhammad A. Kadir, Khandaker R. Islam, Khandaker F. Islam, Zaid B. Mahbub, Mohamed Arselene Ayari, Muhammad E. H. Chowdhury. (2020) "Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization". IEEE Access, Vol. 8, pp 191586 - 191601. DOI. 10.1109/ACCESS.2020.3031384. Paper Link

To view images please check image folders and references of each image are provided in the metadata.csv.

Research Team members and their affiliation Muhammad E. H. Chowdhury, PhD (mchowdhury@qu.edu.qa) Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Tawsifur Rahman (tawsifurrahman.1426@gmail.com) Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Amith Khandakar (amitk@qu.edu.qa) Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Rashid Mazhar, MD Thoracic Surgery, Hamad General Hospital, Doha-3050, Qatar Muhammad Abdul Kadir, PhD Department of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, Bangladesh Zaid Bin Mahbub, PhD Department of Mathematics and Physics, North South University, Dhaka-1229, Bangladesh Khandakar R. Islam, MD Department of Orthodontics, Bangabandhu Sheikh Mujib Medical University, Dhaka-1000, Bangladesh

Contribution - This dataset contains CXR images of Normal (3500) and patients with TB (700 TB images in publicly accessible and 2800 TB images can be downloaded from NIAID TB portal[3] by signing an agreement). The TB database is collected from the source: 1. NLM dataset: National Library of Medicine (NLM) in the U.S. [1] has made two lung X-ray datasets publicly available: the Montgomery and Shenzhen datasets. 2. Belarus dataset: Belarus Set [2] was collected for a drug resistance study initiated by the National Institute of Allergy and Infectious Diseases, Ministry of Health, Republic of Belarus. 3. NIAID TB dataset: NIAID TB portal program dataset [3], which contains about 3000 TB positive CXR images from about 3087 cases. -Note: Due to the data-sharing restriction, we have to direct the potential user to NIAID website where you can get a data-sharing agreement signing option and you can get DICOM images from there easily. Weblink: https://tbportals.niaid.nih.gov/download-data 4. RSNA CXR dataset: RSNA pneumonia detection challenge dataset [4], which is comprised of about 30,000 chest X-ray images, where 10,000 images are normal and others are abnormal and lung opacity images.

This database has been used in the paper titled “Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization” published in IEEE Access in 2020.

Objective - Researchers can use this database to produce useful and impactful scholarly work on TB, which can help in tackling this issue.

Citation - Please cite this database if you are using it for any scientific purpose: Tawsifur Rahman, Amith Khandakar, Muhammad A. Kadir, Khandaker R. Islam, Khandaker F. Islam, Zaid B. Mahbub, Mohamed Arselene Ayari, Muhammad E. H. Chowdhury. (2020) "Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization". IEEE Access, Vol. 8, pp 191586 - 191601. DOI. 10.1109/ACCESS.2020.3031384.

References: [1] S. Jaeger, S. Candemir, S. Antani, Y.-X. J. Wáng, P.-X. Lu, and G. Thoma, "Two public chest X-ray datasets for computer-aided screening of pulmonary diseases," Quantitative imaging in medicine and surgery, vol. 4 (6), p. 475(2014) [2] B. P. Health. (2020). BELARUS TUBERCULOSIS PORTAL [Online]. Available: http://tuberculosis.by/. [Accessed on 09-June-2020] [3] NIAID TB portal program dataset [Online]. Available: https://tbportals.niaid.nih.gov/download-data. [4] kaggle. RSNA Pneumonia Detection Challenge [Online]. Available: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data. [Accessed on 09-June-2020]

Lunar regolith simulants are manufactured in order to provide a higher volume, much less expensive and more available source of material, compared to real lunar regolith material, upon which to test various instruments and machines that are being designed to operate on the lunar surface. There are many sources of these materials. However, the three-dimensional (3D) shape of these materials has never been characterized and used to compare to each other and to real lunar regolith material brought back from the Apollo missions. The focus of this database is to provide 3D shape and size information for each of 17 lunar regolith materials (8 mare, 9 highland). Over 1.1 million particles are in this database, with their 3D shape stored as STL files. Geometric information about each particle is in the database, as well as the original X-ray CT images from which the particles were extracted.

Chest x-ray database from Kaggle consisting of 5863 chest x-ray images chosen from retrospective cohorts of pediatric patients graded by two expert physicians.