MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset

This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Morozov, S. P, Andreychenko, A. E, Pavlov, N. A, Vladzymyrskyy, A. V, Ledikhova, N. V, Gombolevskiy, V. A, Blokhin, I. A, Gelezhe, P. B, Gonchar, A. V, Chernina, V. Yu
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Morozov, S. P
Andreychenko, A. E
Pavlov, N. A
Vladzymyrskyy, A. V
Ledikhova, N. V
Gombolevskiy, V. A
Blokhin, I. A
Gelezhe, P. B
Gonchar, A. V
Chernina, V. Yu
description This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License. Key words: artificial intelligence, COVID-19, machine learning, dataset, CT, chest, imaging
doi_str_mv 10.48550/arxiv.2005.06465
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2005_06465</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2005_06465</sourcerecordid><originalsourceid>FETCH-LOGICAL-a675-7ef2d5fe59a5e4bf8c87f50240707bd0d895d6719c38c11eb35cfa70cddbb98b3</originalsourceid><addsrcrecordid>eNotz81KAzEUhuFsXEj1AlyZG5jxzM-ZJO5KarXQUtChXQ75ObGBOsokiN69tHX1rd4PHsbuKihbiQgPZvqJ32UNgCV0bYfXbL75TBvyC5PNI9cHSpnrnr85Mya-j_nA9Xa3WhSV4q90NJk8X8bRx_E98VOTKN-wq2COiW7_d8b65VOvX4r19nml5-vCdAILQaH2GAiVQWptkE6KgFC3IEBYD14q9J2olGukqyqyDbpgBDjvrVXSNjN2f7k9G4avKX6Y6Xc4WYazpfkD6AtCdQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset</title><source>arXiv.org</source><creator>Morozov, S. P ; Andreychenko, A. E ; Pavlov, N. A ; Vladzymyrskyy, A. V ; Ledikhova, N. V ; Gombolevskiy, V. A ; Blokhin, I. A ; Gelezhe, P. B ; Gonchar, A. V ; Chernina, V. Yu</creator><creatorcontrib>Morozov, S. P ; Andreychenko, A. E ; Pavlov, N. A ; Vladzymyrskyy, A. V ; Ledikhova, N. V ; Gombolevskiy, V. A ; Blokhin, I. A ; Gelezhe, P. B ; Gonchar, A. V ; Chernina, V. Yu</creatorcontrib><description>This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License. Key words: artificial intelligence, COVID-19, machine learning, dataset, CT, chest, imaging</description><identifier>DOI: 10.48550/arxiv.2005.06465</identifier><language>eng</language><subject>Computer Science - Computers and Society ; Computer Science - Learning</subject><creationdate>2020-05</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2005.06465$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2005.06465$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Morozov, S. P</creatorcontrib><creatorcontrib>Andreychenko, A. E</creatorcontrib><creatorcontrib>Pavlov, N. A</creatorcontrib><creatorcontrib>Vladzymyrskyy, A. V</creatorcontrib><creatorcontrib>Ledikhova, N. V</creatorcontrib><creatorcontrib>Gombolevskiy, V. A</creatorcontrib><creatorcontrib>Blokhin, I. A</creatorcontrib><creatorcontrib>Gelezhe, P. B</creatorcontrib><creatorcontrib>Gonchar, A. V</creatorcontrib><creatorcontrib>Chernina, V. Yu</creatorcontrib><title>MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset</title><description>This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License. Key words: artificial intelligence, COVID-19, machine learning, dataset, CT, chest, imaging</description><subject>Computer Science - Computers and Society</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz81KAzEUhuFsXEj1AlyZG5jxzM-ZJO5KarXQUtChXQ75ObGBOsokiN69tHX1rd4PHsbuKihbiQgPZvqJ32UNgCV0bYfXbL75TBvyC5PNI9cHSpnrnr85Mya-j_nA9Xa3WhSV4q90NJk8X8bRx_E98VOTKN-wq2COiW7_d8b65VOvX4r19nml5-vCdAILQaH2GAiVQWptkE6KgFC3IEBYD14q9J2olGukqyqyDbpgBDjvrVXSNjN2f7k9G4avKX6Y6Xc4WYazpfkD6AtCdQ</recordid><startdate>20200513</startdate><enddate>20200513</enddate><creator>Morozov, S. P</creator><creator>Andreychenko, A. E</creator><creator>Pavlov, N. A</creator><creator>Vladzymyrskyy, A. V</creator><creator>Ledikhova, N. V</creator><creator>Gombolevskiy, V. A</creator><creator>Blokhin, I. A</creator><creator>Gelezhe, P. B</creator><creator>Gonchar, A. V</creator><creator>Chernina, V. Yu</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20200513</creationdate><title>MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset</title><author>Morozov, S. P ; Andreychenko, A. E ; Pavlov, N. A ; Vladzymyrskyy, A. V ; Ledikhova, N. V ; Gombolevskiy, V. A ; Blokhin, I. A ; Gelezhe, P. B ; Gonchar, A. V ; Chernina, V. Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a675-7ef2d5fe59a5e4bf8c87f50240707bd0d895d6719c38c11eb35cfa70cddbb98b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Computers and Society</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Morozov, S. P</creatorcontrib><creatorcontrib>Andreychenko, A. E</creatorcontrib><creatorcontrib>Pavlov, N. A</creatorcontrib><creatorcontrib>Vladzymyrskyy, A. V</creatorcontrib><creatorcontrib>Ledikhova, N. V</creatorcontrib><creatorcontrib>Gombolevskiy, V. A</creatorcontrib><creatorcontrib>Blokhin, I. A</creatorcontrib><creatorcontrib>Gelezhe, P. B</creatorcontrib><creatorcontrib>Gonchar, A. V</creatorcontrib><creatorcontrib>Chernina, V. Yu</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Morozov, S. P</au><au>Andreychenko, A. E</au><au>Pavlov, N. A</au><au>Vladzymyrskyy, A. V</au><au>Ledikhova, N. V</au><au>Gombolevskiy, V. A</au><au>Blokhin, I. A</au><au>Gelezhe, P. B</au><au>Gonchar, A. V</au><au>Chernina, V. Yu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset</atitle><date>2020-05-13</date><risdate>2020</risdate><abstract>This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License. Key words: artificial intelligence, COVID-19, machine learning, dataset, CT, chest, imaging</abstract><doi>10.48550/arxiv.2005.06465</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.2005.06465
ispartof
issn
language eng
recordid cdi_arxiv_primary_2005_06465
source arXiv.org
subjects Computer Science - Computers and Society
Computer Science - Learning
title MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T02%3A00%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=MosMedData:%20Chest%20CT%20Scans%20With%20COVID-19%20Related%20Findings%20Dataset&rft.au=Morozov,%20S.%20P&rft.date=2020-05-13&rft_id=info:doi/10.48550/arxiv.2005.06465&rft_dat=%3Carxiv_GOX%3E2005_06465%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true