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Open database of medical images

Open database of medical images


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Does anyone know of an open repository of medical images (e.g., CT scans) organized by disease category? I'm working on some computer vision software that requires a large set of controls from which to learn.


Open-access medical images:

  1. MedPix (by keyword, organic system, pathology and diagnosis A-Z)
  2. Radiopaedia (by keyword)
  3. PEIR Radiology (by keyword, organ or anatomic region)
  4. Ultrasound Cases (by keyword and organic system)
  5. Open Access Biomedical Image Search Engine (Openi) (by keyword)
  6. Wikimedia Commons (by keyword)
  7. Google image search (using the filter "creative commons," "public domain" or "open access")
  8. Google image search (using filters: black/white, noncommercial, photo)

More:


Here's a good start: http://www.aylward.org/notes/open-access-medical-image-repositories. I understand that this question was somewhat googleable, but perhaps amassing a collection of curated resource links is constructive here since a search through the exchange revealed no prior inquiries into this topic.

Update: For those who are interested in understanding the current landscape of tools available for analysis of medical images: Check out this list of open source tools (http://www0.cs.ucl.ac.uk/opensource_mia_ws_2012/links.html).


Like I already referenced on an early post, there are several options on the DICOM Library. It is easy to find whatever image modality you want and also whatever disease category.


15 Open Datasets for Healthcare

Machine Learning is exploding into the world of healthcare. When we talk about the ways ML will revolutionize certain fields, healthcare is always one of the top areas seeing huge strides, thanks to the processing and learning power of machines. There’s a good chance you either are or will soon be employed in the healthcare field. A while back, I wrote a list of 25 excellent open datasets for ML and included healthdata.gov and MIMIC Critical Care Database. Here are 15 more excellent datasets specifically for healthcare.

[Gain the dat a science skills you need to get ahead with Ai+! Learn more here]


Natural-Image Datasets

    : The most commonly used sanity check. Dataset of 25x25, centered, B&W handwritten digits. It is an easy task — just because something works on MNIST, doesn’t mean it works. : 32x32 color images with 10 / 100 categories. Not commonly used anymore, though once again, can be an interesting sanity check. : Pictures of objects belonging to 101 categories. : Pictures of objects belonging to 256 categories. : is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. Like CIFAR-10 with some modifications. : House numbers from Google Street View. Think of this as recurrent MNIST in the wild. : Binocular images of toy figurines under various illumination and pose. : Generic image Segmentation / classification — not terribly useful for building real-world image annotation, but great for baselines : A large dataset of annotated images. : The de-facto image dataset for new algorithms. Many image API companies have labels from their REST interfaces that are suspiciously close to the 1000 category WordNet hierarchy from ImageNet. : Scene understanding with many ancillary tasks (room layout estimation, saliency prediction, etc.) and an associated competition. : Generic image understanding / captioning, with an associated competition. : Different objects imaged at every angle in a 360 rotation. : Different objects imaged at every angle in a 360 rotation. : A collection of 9 million URLs to images “that have been annotated with labels spanning over 6,000 categories” under Creative Commons.

Geospatial data

    : Vector data for the entire planet under a free license. It contains (an older version of) the US Census Bureau’s data. : Satellite shots of the entire Earth surface, updated every several weeks. : Doppler radar scans of atmospheric conditions in the US.

2. Flickr

Flickr is a great source for finding lots of photos, especially those that are free to use on your blog. Many leading open source image sites such as Photo pin use the Flickr API in order to curate images onto their site. It’s no surprise Flickr is used as the de-facto site for finding free images due to how many they have on display.

The main downsides to Flickr are its below par search functionality, the hard-to-find ‘download’ button and the fact you have to generate the attribution link to the author yourself.

How To Use Flickr to find images for your blog

Using Flickr to find open sourced images is quite simple. Just be sure to check that you are using an image that can be used on your own website.

When searching for an image – you must be sure the “creative commons only” option is selected.

Each image on Flickr has its own license, however if you want to use images that allow modification – be sure the “modifications allowed” option is ticked to display images that allow for adaptation.

Unlike Photo pin, with Flickr you need to go an extra step in order to retrieve the attribution link for an image.

Click the “some rights reserved” link to read the license.

To download an image from Flickr, you must click the download icon to display all the sizes the image is available in for downloading.


Open multiple databases at the same time

In a single instance of Access, you can have only one database open at a time. In other words, you cannot start Access, open one database, and then open another database without closing the first database. However, you can run multiple instances of Access at the same time, each with a database open in it. Each time you start Access, you open a new instance of it. For example, to have two Access databases open at the same time, start Access and open the first Access database, and then start a new instance of Access and open the second database.

Note: The number of instances of Access that you can run at the same time is limited by how much memory is available. Available memory depends on how much RAM your computer has and how much memory is being used by the other programs running at the time.

Each instance of Access runs in a separate window. If you have more than one instance of Access running and you want to view them simultaneously, you can tile the windows.


6 Sources of Free Images for Science Blogging

If you blog, you probably know that most online images are copyrighted and off-limits for your site. Where is an enterprising science writer to turn for artwork that is free, beautiful, and legally bloggable?

1. Ask the artist

Artists own their copyrights, but that doesn't mean many aren't happy to share! Often, permission for non-commercial or personal blog use costs a mere link back to the artist's website. While taking 30 seconds to compose a brief email may seem like extra work, consider that symbioses between writers and artists often benefit both (exhibit A: Primate Diaries blog and Nathaniel Gold). Don't be shy!

Government agencies place most of their images in the public domain. As they should- the public paid for them! NASA maintains a wondrous database of space-related imagery usable for most purposes so long as you do not imply government endorsement of a commercial product.

The U.S. Geological Survey curates a sizeable collection of public domain photographs covering not just rocks, volcanos, national parks, and earthquakes, but fascinating early images of native cultures, political figures, historical events, and more. Like NASA's archive, USGS photographs are free to use without prior permission.

4. NIH Images from the History of Medicine

The National Institute of Health's historical archives contain 70,000 images, including photographs, cartoons, paintings, public health posters, and other miscellanea.

5. Public Health Image Library

The Centers for Disease Control hosts PHIL, the Public Health Image Library. Most of the content is an unusual combination of being both modern and in the public domain, but check the details before use, as a few of PHIL's treasures are copyrighted.

6. Wikimedia Commons

The Wikimedia Commons is a giant repository covering quite literally millions of items of varying copyright status, including public domain and an assortment of Creative Commons-licensed copyrighted work. Creative Commons material may or may not be safe to copy depending on the particulars of your intended use and the rights-holders' interpretation of "non-commercial", so I recommend sticking to the public domain.

The views expressed are those of the author(s) and are not necessarily those of Scientific American.

ABOUT THE AUTHOR(S)

Alex Wild is Curator of Entomology at the University of Texas at Austin, where he studies the evolutionary history of ants. In 2003 he founded a photography business as an aesthetic complement to his scientific work, and his natural history photographs appear in numerous museums, books and media outlets.


Research in the NCBI Computational Biology Branch (CBB) focuses on theoretical, analytical, and applied computational approaches to a broad range of fundamental problems in molecular biology and medicine.

The research program in the Computational Biology Branch is carried out by Senior Investigators, tenure track Investigators, Staff Scientists, Postdoctoral Fellows, and students. The program focuses on theoretical, analytical and applied approaches to a broad range of fundamental problems in molecular biology.

The expertise of the group is concentrated in sequence analysis, protein structure/function analysis, chemical informatics, and genome analysis. Research interests further cover a wide range of topics in computational biology and information science. These include, but are not limited to, database searching algorithms, sequence signal identification, mathematical models of evolution, statistical methods in virology, dynamic behavior of chemical reaction systems, statistical text-retrieval algorithms, protein structure and function prediction, comparative genomics, taxonomic trees, population genetics, and systems biology.

Many of the basic research projects conducted by CBB investigators serve to enhance and strengthen NCBI's suite of publicly available databases and software application tools. Collaborative research efforts, among NCBI investigators as well as with the external research community, have led to the development of innovative algorithms (BLAST, PSI-BLAST, VAST, and COGs), novel research approaches (text neighboring) and fundamental resources (PubChem and CDD) that have transformed the field of computational biology. Algorithms and applications currently under development have the potential to further advance scientific discovery.

Members of the CBB contribute significantly to the validity and reliability of NCBI's online resources by reviewing the quality and accuracy of the data deposited in the databases, as well as the accuracy of the information used to annotate the data. Members also provide leadership and guidance to the extramural community by planning and organizing scientific consortia to determine the most effective use of public sequence resources for large-scale or high-throughput experimental biology. Researchers collaborate to define new areas of research and identify appropriate computational mechanisms to address them.


Clinical Case Reports

*2019 Journal Impact Factor was established by dividing the number of articles published in 2017 and 2018 with the number of times they are cited in 2019 based on Google Scholar Citation Index database. If 'X' is the total number of articles published in 2017 and 2018, and 'Y' is the number of times these articles were cited in indexed journals during 2019 then, journal impact factor = Y/X

The Journal of Clinical Case Reports is a peer reviewed Journal that publishes novel research work conducted as case reports in the medical field on various types of diseases, covering their respective clinical and diagnosis issues.

The Journal of Clinical Case Reports is an Open Access Scientific Journal that offers an interesting publishing platform globally and aims to keep scientists, clinicians and medical practitioners, researchers, and students informed and updated on the ongoing research in the relevant area.Outstanding quality articles are welcome to maintain the highest standard of the journal and to achieve high impact factor.

Journal of Clinical Case reports is using Tracking System for maintaining the quality in peer review process. Tracking is an online manuscript submission,review and tracking system. Review processing is performed by the editorial board members of Journal of Clinical Case Reports or by outside experts. At least two independent reviewers approval followed by editor approval is required for acceptance of any citable manuscript. Authors may submit manuscripts and track their progress through the system, hopefully to publication. Reviewers can download manuscripts and submit their opinions to the editor. Editors can manage the whole submission/review/revise/publish process.


Open-Access Data and Computational Resources to Address COVID-19

COVID-19 open-access data and computational resources are being provided by federal agencies, including NIH, public consortia, and private entities. These resources are freely available to researchers, and this page will be updated as more information becomes available.

The Office of Data Science Strategy seeks to provide the research community with links to open-access data, computational, and supporting resources. These resources are being aggregated and posted for scientific and public health interests. Inclusion of a resource on this list does not mean it has been evaluated or endorsed by NIH.

To suggest a new resource, please send an email with the name of the resource, the website, and a short description to [email protected]

NIAID Clinical Trials Data Repository, [email protected], is a NIAID cloud-based, secure data platform that enables sharing of and access to reports and data sets from NIAID COVID-19 and other sponsored clinical trials for the basic and clinical research community.

A centralized repository of up-to-date and curated datasets on or related to the spread and characteristics of SARS-CoV-2 and COVID-19. Information on how to best use this resource is available.

The Broad Terra cloud workspace for best practices with COVID-19 genomics data

  • Raw COVID-19 sequencing data from the NCBI Sequence Read Archive (SRA)
  • Workflows for genome assembly, quality control, metagenomic classification, and aggregate statistics
  • Jupyter Notebook produces quality control plots for workflow output

The open source dataset of nearly 50,000 chemical substances includes antiviral drugs and related compounds that are structurally similar to known antivirals for use in applications including research, data mining, machine learning and analytics. A COVID-19 Protein Target Thesaurus is also available. CAS is a division of the American Chemical Society.

The CDC is providing a variety of data on COVID-19 in the United States.

Maintained by China National Center for Bioinformation/National Genomics Data Center, 2019nCoVR is a comprehensive resource on COVID-19, combining up-to-date information on all published sequences, mutation analyses, literatures and others.

View listed clinical studies related to the coronavirus disease (COVID-19). Studies are submitted in a structured format directly by the sponsors and investigators conducting the studies. Submitted study information is generally posted on ClinicalTrials.gov within 2 days after initial submission and site content is updated daily. Full website content is also available through the API.

This collection of files contains information for printing 3D physical models of SARS-CoV-2 proteins and is part of the NIH 3D Print Exchange.

Freely available dataset of 45,000 scholarly articles, including over 33,000 with full text, on COVID-19, SARS-CoV-2, and related coronaviruses. This machine-readable resource is provided to enable the application of natural language processing and other AI techniques.

See the CORD-19 Challenge, developed in partnership with Kaggle. Amazon Web Services has a CORD-19 search website.

Read the accompanying call to action from the White House Office of Science & Technology Policy and learn more about the creation of CORD-19.

This web-based viewer offers 3D visualization and analysis of SARS-CoV-2 protein structures with respect to the CoV-2 mutational patterns.

The COVID-DPR provides whole slide images of histopathologic samples relevant to COVID-19, including biopsy samples and autopsy specimens. The current focus of the repository includes tissue from the lungs, heart, liver, and kidney. The repository contains examples of H1N1, SARS, and MERS for comparison.

The NCI Cancer Imaging Program (CIP) is utilizing its Cancer Imaging Archive as a resource for making COVID-19 radiology and digitized histopathology patient image sets publicly available.

A centralized sequence repository for all strains of novel corona virus (SARS-CoV-2) submitted to the National Center for Biotechnology Information (NCBI). Included are both the original sequences submitted by the principal investigator as well as SRA-processed sequences that require the SRA Toolkit for analysis.

All Dimensions publications, datasets, and clinical trials related to COVID-19, updated daily. Content exported from the openly accessible Dimensions application accessible at https://covid-19.dimensions.ai/.

The European Bioinformatics Institute (EMBL-EBI), part of the European Molecular Biology Laboratory, has a COVID-19 Data Portal to facilitate data sharing and analysis and ultimately contribute to the European COVID-19 Data Platform. EMBL-EBI is part of the International Nucleotide Sequence Database Collaboration (INSDC) the National Center for Biotechnology Information (NCBI) is the U.S. partner of the INSDC.

The downloadable data file is updated daily and contains the latest available public data on COVID-19. Each row/entry contains the number of new cases reported per day and per country. You may use the data in line with ECDC’s copyright policy.

Provides rapid, open, and unrestricted access to virus nucleotide sequences and is the repository being recommended by NIAID and CDC for investigator and public health submissions. Due to the scale of data indexing, there may be a delay before new submissions are indexed and retrievable with a term-based query.

Provides rapid, open, and unrestricted access to virus conceptually translated protein sequences and is the repository being recommended by NIAID and CDC for investigator and public health submissions. Due to the scale of data indexing, there may be a delay before new submissions are indexed and retrievable with a term-based query.

Human transcriptional responses to SARS-CoV-2 infection

International database of hCoV-19 genome sequences and related clinical and epidemiological data

GCP is hosting a repository of public datasets and offering free hosting and queries of COVID datasets. Learn more about the free hosting and queries of COVID datasets.

Comprehensive, expert-curated portfolio of COVID‑19 publications and preprints that includes peer-reviewed articles from PubMed and preprints from medRxiv, bioRxiv, ChemRxiv, and arXiv.

NLM curated literature hub for COVID-19

NIGMS-funded modeling research. Public-access data collections with documented metadata.

NCATS is generating a collection of datasets by screening a panel of SARS-CoV-2-related assays against all approved drugs. These datasets, as well as the assay protocols used to generate them, are being made immediately available to the scientific community on this site as these screens are completed.

SARS-CoV-2 focused content from NCBI Virus, including links to related resources. Search, filter, and download the most up-to-date nucleotide and protein sequences from GenBank and RefSeq (taxid 2697049). Generate multiple sequence alignments and phylogenetic trees for sequences of interest. Provides one-click access to the Betacoronavirus BLAST database and relevant literature in PubMed.

Open-source SARS-CoV-2 genome data and analytic and visualization tools

The Inter-university Consortium for Political and Social Research (ICPSR) has launched a new repository of data examining the impact of the novel coronavirus global pandemic. This repository is a free, self-publishing option for researchers to share COVID-19 related data.

A resource to aggregate data critical to scientific research during outbreaks of emerging diseases, such as COVID-19

Small molecule compounds, bioactivity data, biological targets, bioassays, chemical substances, patents, and pathways

On March 13, national science and technology advisors from a dozen countries, including the United States, called on publishers to voluntarily agree to make their COVID-19 and coronavirus-related publications, and the available data supporting them, immediately accessible in PMC and other appropriate public repositories to support the ongoing public health emergency response efforts. The articles added to PMC are distributed through the PMC Open Access Subset and are made available in CORD-19.

The RCSB Protein Data Bank is offering access to COVID-19 related PDB structures for research and related images and videos for education.

Reactome is a free, open-source, curated and peer-reviewed pathway database. The goal is to provide intuitive bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modeling, systems biology and education. In response to the COVID-19 pandemic, Reactome is fast-tracking the annotation of human coronavirus infection pathways.

A database of carefully validated SARS-CoV-2 protein structures, including many structural models which have been re-refined or re-processed. The resource is being updated weekly by Minor Lab at the University of Virginia as new SARS-CoV-2 structures are being deposited to the Protein Data Bank.

Provides rapid, open, and unrestricted access to virus nucleotide or metagenomic sequence data and is the repository being recommended by NIAID and CDC for investigator and public health submissions. Due to the scale of data indexing, there may be a delay before new submissions are indexed and retrievable with a term-based query.


Open database of medical images - Biology

Vision Group, University of Massachusetts

Vision Group (Carla Brodley, brodley '@' cs.umass.edu)

The instances were drawn randomly from a database of 7 outdoor images. The images were handsegmented to create a classification for every pixel.

Each instance is a 3x3 region.

Attribute Information:

1. region-centroid-col: the column of the center pixel of the region.
2. region-centroid-row: the row of the center pixel of the region.
3. region-pixel-count: the number of pixels in a region = 9.
4. short-line-density-5: the results of a line extractoin algorithm that counts how many lines of length 5 (any orientation) with low contrast, less than or equal to 5, go through the region.
5. short-line-density-2: same as short-line-density-5 but counts lines of high contrast, greater than 5.
6. vedge-mean: measure the contrast of horizontally adjacent pixels in the region. There are 6, the mean and standard deviation are given. This attribute is used as a vertical edge detector.
7. vegde-sd: (see 6)
8. hedge-mean: measures the contrast of vertically adjacent pixels. Used for horizontal line detection.
9. hedge-sd: (see 8).
10. intensity-mean: the average over the region of (R + G + B)/3
11. rawred-mean: the average over the region of the R value.
12. rawblue-mean: the average over the region of the B value.
13. rawgreen-mean: the average over the region of the G value.
14. exred-mean: measure the excess red: (2R - (G + B))
15. exblue-mean: measure the excess blue: (2B - (G + R))
16. exgreen-mean: measure the excess green: (2G - (R + B))
17. value-mean: 3-d nonlinear transformation of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals of Interactive Computer Graphics)
18. saturatoin-mean: (see 17)
19. hue-mean: (see 17)

Anthony K H Tung and Xin Xu and Beng Chin Ooi. CURLER: Finding and Visualizing Nonlinear Correlated Clusters. SIGMOD Conference. 2005. [View Context].

Xiaoli Z. Fern and Carla Brodley. Cluster Ensembles for High Dimensional Clustering: An Empirical Study. Journal of Machine Learning Research n, a. 2004. [View Context].

Aristidis Likas and Nikos A. Vlassis and Jakob J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, 36. 2003. [View Context].

Manoranjan Dash and Huan Liu and Peter Scheuermann and Kian-Lee Tan. Fast hierarchical clustering and its validation. Data Knowl. Eng, 44. 2003. [View Context].

Adil M. Bagirov and Alex Rubinov and A. N. Soukhojak and John Yearwood. Unsupervised and supervised data classification via nonsmooth and global optimization. School of Information Technology and Mathematical Sciences, The University of Ballarat. [View Context].

K. A. J Doherty and Rolf Adams and Neil Davey. Unsupervised Learning with Normalised Data and Non-Euclidean Norms. University of Hertfordshire. [View Context].

Adil M. Bagirov and John Yearwood. A new nonsmooth optimization algorithm for clustering. Centre for Informatics and Applied Optimization, School of Information Technology and Mathematical Sciences, University of Ballarat. [View Context].

K. A. J Doherty and Rolf Adams and Neil Davey. Non-Euclidean Norms and Data Normalisation. Department of Computer Science, University of Hertfordshire, College Lane. [View Context].

Michael Lindenbaum and Shaul Markovitch and Dmitry Rusakov. Selective Sampling Using Random Field Modelling. [View Context].

James Tin and Yau Kwok. Moderating the Outputs of Support Vector Machine Classifiers. Department of Computer Science Hong Kong Baptist University Hong Kong. [View Context].

Thomas T. Osugi and M. S. EXPLORATION-BASED ACTIVE MACHINE LEARNING. Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements. [View Context].

Nikos A. Vlassis and Aristidis Likas. A greedy EM algorithm for Gaussian mixture. Intelligent Autonomous Systems, IAS. [View Context].

Amund Tveit. Empirical Comparison of Accuracy and Performance for the MIPSVM classifier with Existing Classifiers. Division of Intelligent Systems Department of Computer and Information Science, Norwegian University of Science and Technology. [View Context].

Je Scott and Mahesan Niranjan and Richard W. Prager. Realisable Classifiers: Improving Operating Performance on Variable Cost Problems. Cambridge University Department of Engineering. [View Context].

C. Titus Brown and Harry W. Bullen and Sean P. Kelly and Robert K. Xiao and Steven G. Satterfield and John G. Hagedorn and Judith E. Devaney. Visualization and Data Mining in an 3D Immersive Environment: Summer Project 2003. [View Context].

Papers That Cite This Data Set 1 :

Anthony K H Tung and Xin Xu and Beng Chin Ooi. CURLER: Finding and Visualizing Nonlinear Correlated Clusters. SIGMOD Conference. 2005. [View Context].

Xiaoli Z. Fern and Carla Brodley. Cluster Ensembles for High Dimensional Clustering: An Empirical Study. Journal of Machine Learning Research n, a. 2004. [View Context].

Aristidis Likas and Nikos A. Vlassis and Jakob J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, 36. 2003. [View Context].

Manoranjan Dash and Huan Liu and Peter Scheuermann and Kian-Lee Tan. Fast hierarchical clustering and its validation. Data Knowl. Eng, 44. 2003. [View Context].

Adil M. Bagirov and Alex Rubinov and A. N. Soukhojak and John Yearwood. Unsupervised and supervised data classification via nonsmooth and global optimization. School of Information Technology and Mathematical Sciences, The University of Ballarat. [View Context].

K. A. J Doherty and Rolf Adams and Neil Davey. Unsupervised Learning with Normalised Data and Non-Euclidean Norms. University of Hertfordshire. [View Context].

Adil M. Bagirov and John Yearwood. A new nonsmooth optimization algorithm for clustering. Centre for Informatics and Applied Optimization, School of Information Technology and Mathematical Sciences, University of Ballarat. [View Context].

K. A. J Doherty and Rolf Adams and Neil Davey. Non-Euclidean Norms and Data Normalisation. Department of Computer Science, University of Hertfordshire, College Lane. [View Context].

Michael Lindenbaum and Shaul Markovitch and Dmitry Rusakov. Selective Sampling Using Random Field Modelling. [View Context].

James Tin and Yau Kwok. Moderating the Outputs of Support Vector Machine Classifiers. Department of Computer Science Hong Kong Baptist University Hong Kong. [View Context].

Thomas T. Osugi and M. S. EXPLORATION-BASED ACTIVE MACHINE LEARNING. Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements. [View Context].

Nikos A. Vlassis and Aristidis Likas. A greedy EM algorithm for Gaussian mixture. Intelligent Autonomous Systems, IAS. [View Context].

Amund Tveit. Empirical Comparison of Accuracy and Performance for the MIPSVM classifier with Existing Classifiers. Division of Intelligent Systems Department of Computer and Information Science, Norwegian University of Science and Technology. [View Context].

Je Scott and Mahesan Niranjan and Richard W. Prager. Realisable Classifiers: Improving Operating Performance on Variable Cost Problems. Cambridge University Department of Engineering. [View Context].

C. Titus Brown and Harry W. Bullen and Sean P. Kelly and Robert K. Xiao and Steven G. Satterfield and John G. Hagedorn and Judith E. Devaney. Visualization and Data Mining in an 3D Immersive Environment: Summer Project 2003. [View Context].


Watch the video: DICOM ViewerMedical Image Manipulation (July 2022).


Comments:

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  3. Gabi

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