{"id":"https://openalex.org/W4205106659","doi":"https://doi.org/10.1109/smc52423.2021.9659170","title":"A Non-local Hierarchical Refinement Fully Convolutional Network for COVID-19 Infected Region Segmentation","display_name":"A Non-local Hierarchical Refinement Fully Convolutional Network for COVID-19 Infected Region Segmentation","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W4205106659","doi":"https://doi.org/10.1109/smc52423.2021.9659170"},"language":"en","primary_location":{"id":"doi:10.1109/smc52423.2021.9659170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc52423.2021.9659170","pdf_url":null,"source":{"id":"https://openalex.org/S4363607761","display_name":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5100422662","display_name":"Hongyu Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyu Wang","raw_affiliation_strings":["School of Computer Science, Sichuan Normal University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Sichuan Normal University, Chengdu, China","institution_ids":["https://openalex.org/I63354593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055218366","display_name":"Nan Mu","orcid":"https://orcid.org/0000-0003-0476-7500"},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Mu","raw_affiliation_strings":["School of Computer Science, Sichuan Normal University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Sichuan Normal University, Chengdu, China","institution_ids":["https://openalex.org/I63354593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100433581","display_name":"Yu Zhang","orcid":"https://orcid.org/0000-0002-5848-8892"},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zhang","raw_affiliation_strings":["School of Computer Science, Sichuan Normal University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Sichuan Normal University, Chengdu, China","institution_ids":["https://openalex.org/I63354593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102077278","display_name":"Heng Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I63354593","display_name":"Sichuan Normal University","ror":"https://ror.org/043dxc061","country_code":"CN","type":"education","lineage":["https://openalex.org/I63354593"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng Yang","raw_affiliation_strings":["School of Computer Science, Sichuan Normal University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Sichuan Normal University, Chengdu, China","institution_ids":["https://openalex.org/I63354593"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I63354593"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2775","last_page":"2780"},"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/T10862","display_name":"AI in cancer detection","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9900000095367432,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7517615556716919},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6135628819465637},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.612755537033081},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5911533236503601},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5445584654808044},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.46224451065063477},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4395640790462494},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43481940031051636},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32146063446998596},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08373942971229553}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7517615556716919},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6135628819465637},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.612755537033081},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5911533236503601},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5445584654808044},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.46224451065063477},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4395640790462494},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43481940031051636},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32146063446998596},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08373942971229553},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc52423.2021.9659170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc52423.2021.9659170","pdf_url":null,"source":{"id":"https://openalex.org/S4363607761","display_name":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8600000143051147}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1994922096","https://openalex.org/W2194775991","https://openalex.org/W2804383999","https://openalex.org/W2884436604","https://openalex.org/W2939217524","https://openalex.org/W2961348656","https://openalex.org/W2990984982","https://openalex.org/W3011041531","https://openalex.org/W3012867892","https://openalex.org/W3013019084","https://openalex.org/W3013130152","https://openalex.org/W3015388942","https://openalex.org/W3017855299","https://openalex.org/W3019613751","https://openalex.org/W3024045805","https://openalex.org/W3027763298","https://openalex.org/W3033272814","https://openalex.org/W3035740374","https://openalex.org/W3039499593","https://openalex.org/W3040660552","https://openalex.org/W3046359959","https://openalex.org/W3085306326","https://openalex.org/W3120935246","https://openalex.org/W3139487216","https://openalex.org/W3162351260","https://openalex.org/W3168341999","https://openalex.org/W6637373629","https://openalex.org/W6639824700","https://openalex.org/W6753182481"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"To":[0],"control":[1],"the":[2,14,25,34,37,44,56,77,85,118,126,141,149,152,157,163,178],"spread":[3],"of":[4,10,16,27,36,48,55,84,121,151,160,191],"COVID-19,":[5,28],"accurate":[6],"and":[7,19,46,110,128,184],"efficient":[8],"diagnosis":[9,26],"suspected":[11],"cases":[12],"is":[13,30,41,88,174],"crux":[15],"appropriate":[17],"quarantine":[18],"prompt":[20],"treatment.":[21],"In":[22,170],"addition":[23],"to":[24,32,43,62,71,100,115,139,177],"it":[29],"vital":[31],"distinguish":[33],"severity":[35],"confirmed":[38],"cases,":[39],"which":[40],"conducive":[42],"selection":[45],"planning":[47],"treatment":[49],"methods.":[50],"At":[51],"a":[52,64,89,96,111,134,187],"critical":[53],"juncture":[54],"epidemic":[57],"crisis,":[58],"we":[59,94],"are":[60,131],"committed":[61],"developing":[63],"non-local":[65,97,127],"hierarchical":[66],"refinement":[67,113],"fully":[68],"convolutional":[69],"network":[70],"assist":[72],"experts":[73],"in":[74,155],"automatically":[75],"segmenting":[76],"COVID-19-caused":[78],"pneumonia":[79],"infection":[80],"regions.":[81],"The":[82],"architecture":[83],"proposed":[86,153],"model":[87,114,154,173],"deep":[90],"encoder-decoder":[91],"framework.":[92],"Specifically,":[93],"exploit":[95],"perception":[98],"module":[99],"capture":[101],"more":[102],"complementary":[103],"coarse-structure":[104],"information":[105,120],"from":[106],"different":[107],"pyramid":[108],"levels":[109],"local":[112,129],"explicitly":[116],"heighten":[117],"fine-detail":[119],"each":[122],"convolution":[123],"layer.":[124],"Moreover,":[125],"features":[130],"aggregated":[132],"through":[133],"boundary-aware":[135],"multiple":[136],"supervision":[137],"strategy":[138],"produce":[140],"gratifying":[142],"edge-preserving":[143],"segmentation":[144,180],"map.":[145],"Comprehensive":[146],"experiments":[147],"demonstrate":[148],"effectiveness":[150],"boosting":[156],"learning":[158],"ability":[159],"accurately":[161],"identify":[162],"lung":[164],"infected":[165],"regions":[166],"with":[167],"clear":[168],"contours.":[169],"particular,":[171],"our":[172],"superior":[175],"remarkably":[176],"state-of-the-art":[179],"models":[181],"both":[182],"quantitatively":[183],"qualitatively":[185],"on":[186],"real":[188],"CT":[189],"dataset":[190],"COVID-19.":[192]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}