{"id":"https://openalex.org/W4205514339","doi":"https://doi.org/10.1080/21681163.2021.2015447","title":"Fully automated segmentation of pneumonia infection based on Probabilistic Graphical Model and U-Net blend network","display_name":"Fully automated segmentation of pneumonia infection based on Probabilistic Graphical Model and U-Net blend network","publication_year":2022,"publication_date":"2022-01-06","ids":{"openalex":"https://openalex.org/W4205514339","doi":"https://doi.org/10.1080/21681163.2021.2015447"},"language":"en","primary_location":{"id":"doi:10.1080/21681163.2021.2015447","is_oa":false,"landing_page_url":"https://doi.org/10.1080/21681163.2021.2015447","pdf_url":null,"source":{"id":"https://openalex.org/S2764763012","display_name":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","issn_l":"2168-1163","issn":["2168-1163","2168-1171"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031634218","display_name":"Xunpeng Xia","orcid":"https://orcid.org/0000-0002-4390-8946"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xunpeng Xia","raw_affiliation_strings":["School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-4390-8946","affiliations":[{"raw_affiliation_string":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084480853","display_name":"Rongfu Zhang","orcid":"https://orcid.org/0000-0002-1387-7376"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongfu Zhang","raw_affiliation_strings":["School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091896070","display_name":"Xufeng Yao","orcid":"https://orcid.org/0000-0003-2415-4488"},"institutions":[{"id":"https://openalex.org/I4210135985","display_name":"Shanghai University of Medicine and Health Sciences","ror":"https://ror.org/03ns6aq57","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xufeng Yao","raw_affiliation_strings":["College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2415-4488","affiliations":[{"raw_affiliation_string":"College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China","institution_ids":["https://openalex.org/I4210135985"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100726056","display_name":"Gang Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210135985","display_name":"Shanghai University of Medicine and Health Sciences","ror":"https://ror.org/03ns6aq57","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135985"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Huang","raw_affiliation_strings":["College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China","institution_ids":["https://openalex.org/I4210135985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000957942","display_name":"Tiequn Tang","orcid":"https://orcid.org/0000-0002-7438-6474"},"institutions":[{"id":"https://openalex.org/I148128674","display_name":"University of Shanghai for Science and Technology","ror":"https://ror.org/00ay9v204","country_code":"CN","type":"education","lineage":["https://openalex.org/I148128674"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiequn Tang","raw_affiliation_strings":["School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China","institution_ids":["https://openalex.org/I148128674"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5031634218"],"corresponding_institution_ids":["https://openalex.org/I148128674"],"apc_list":null,"apc_paid":null,"fwci":0.1472,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43179713,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10","issue":"6","first_page":"608","last_page":"615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.6942704916000366},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6886796355247498},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6184064745903015},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5337043404579163},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5335776209831238},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5143426656723022},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5097333788871765},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4108462929725647},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3802259564399719},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2822491526603699}],"concepts":[{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.6942704916000366},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6886796355247498},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6184064745903015},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5337043404579163},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5335776209831238},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5143426656723022},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5097333788871765},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4108462929725647},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3802259564399719},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2822491526603699}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/21681163.2021.2015447","is_oa":false,"landing_page_url":"https://doi.org/10.1080/21681163.2021.2015447","pdf_url":null,"source":{"id":"https://openalex.org/S2764763012","display_name":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","issn_l":"2168-1163","issn":["2168-1163","2168-1171"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[{"id":"https://openalex.org/G4513858985","display_name":"\u57fa\u4e8e\u667a\u80fd\u5f71\u50cf\u7ec4\u5b66\u6280\u672f\u7684\u963f\u5c14\u8328\u6d77\u9ed8\u75c5\u65e9\u671f\u9884\u6d4b\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61971275","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2153364064","https://openalex.org/W2592152820","https://openalex.org/W2617267541","https://openalex.org/W2791888607","https://openalex.org/W2884436604","https://openalex.org/W2916412824","https://openalex.org/W2964227007","https://openalex.org/W3001118548","https://openalex.org/W3006082171","https://openalex.org/W3007497549","https://openalex.org/W3010030563","https://openalex.org/W3010659930","https://openalex.org/W3013564598","https://openalex.org/W3014337038","https://openalex.org/W3014725478","https://openalex.org/W3019186020","https://openalex.org/W3045802155","https://openalex.org/W3087854885","https://openalex.org/W3095922816","https://openalex.org/W3104427612","https://openalex.org/W3104810384","https://openalex.org/W3108591672","https://openalex.org/W3108672867","https://openalex.org/W3115781494","https://openalex.org/W3124512534","https://openalex.org/W3128011703","https://openalex.org/W3154493450","https://openalex.org/W3157911332","https://openalex.org/W3158420105"],"related_works":["https://openalex.org/W2109986081","https://openalex.org/W4297589944","https://openalex.org/W2417308975","https://openalex.org/W2964129930","https://openalex.org/W4368755698","https://openalex.org/W4285040766","https://openalex.org/W4388627352","https://openalex.org/W1808888439","https://openalex.org/W4286900141","https://openalex.org/W1522196789"],"abstract_inverted_index":{"COVID-19":[0],"spread":[1],"rapidly":[2],"in":[3,102,121],"the":[4,24,74,79,90,103,118,127,138],"global":[5],"world,":[6],"causing":[7],"a":[8],"serious":[9],"medical":[10],"treatment":[11,26],"crisis.":[12],"Automated":[13],"segmentation":[14,39,124],"of":[15,99,135],"pulmonary":[16,122],"infection":[17,38,105,123],"from":[18,61,69],"Computed":[19],"Tomography":[20],"(CT)":[21],"images":[22],"strengthened":[23],"traditional":[25],"strategy":[27],"against":[28],"COVID-19.":[29],"We":[30],"proposed":[31,91],"an":[32],"automated":[33],"fully":[34],"lung":[35],"CT":[36],"image":[37],"framework":[40,49,93,116],"named":[41],"Probabilistic":[42],"Graphical":[43],"Model":[44],"U-Net":[45,65],"(PGM-U-Net).":[46],"The":[47,64],"whole":[48],"iterative":[50],"training":[51],"end-to-end":[52],"with":[53,57],"general":[54],"back-propagation":[55],"mechanism":[56],"minor":[58],"computational":[59],"overhead":[60],"PGM":[62,75],"component.":[63,76],"feature":[66],"extractor":[67],"benefits":[68],"modelling":[70,83],"spatial":[71,84],"correlations":[72],"through":[73],"Compared":[77],"to":[78],"baseline":[80],"network":[81],"without":[82],"correlations,":[85],"experimental":[86],"results":[87],"illustrate":[88],"that":[89,114],"PGM-U-Net":[92],"achieves":[94,126],"higher":[95],"accuracy":[96],"probability":[97],"maps":[98],"region":[100],"predictions":[101],"isolated":[104],"regions.":[106],"For":[107],"further":[108],"quantitative":[109],"comparison":[110],"experiment,":[111],"we":[112],"demonstrate":[113],"our":[115],"outperforms":[117],"existing":[119],"methods":[120],"and":[125],"Free-response":[128],"Receiver":[129],"Operating":[130],"Characteristic":[131],"Curve":[132],"(FROC)":[133],"score":[134],"0.912":[136],"on":[137],"test":[139],"data":[140],"set.":[141]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-06T09:05:17.133730","created_date":"2025-10-10T00:00:00"}
