{"id":"https://openalex.org/W3033272814","doi":"https://doi.org/10.1109/tmi.2020.3000314","title":"A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images","display_name":"A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images","publication_year":2020,"publication_date":"2020-06-05","ids":{"openalex":"https://openalex.org/W3033272814","doi":"https://doi.org/10.1109/tmi.2020.3000314","mag":"3033272814","pmid":"https://pubmed.ncbi.nlm.nih.gov/32730215"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2020.3000314","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2020.3000314","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8544954","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029722566","display_name":"Guotai Wang","orcid":"https://orcid.org/0000-0002-8632-158X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guotai Wang","raw_affiliation_strings":["School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-8632-158X","affiliations":[{"raw_affiliation_string":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115589085","display_name":"Xinglong Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinglong Liu","raw_affiliation_strings":["SenseTime Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SenseTime Research, Beijing, China","institution_ids":["https://openalex.org/I4210128910"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101451206","display_name":"Chaoping Li","orcid":"https://orcid.org/0009-0009-9642-2358"},"institutions":[{"id":"https://openalex.org/I4210119785","display_name":"Zoucheng People's Hospital","ror":"https://ror.org/02exfk080","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210119785"]},{"id":"https://openalex.org/I4210147206","display_name":"Rongcheng City People's Hospital","ror":"https://ror.org/04d1vhw60","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210147206"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoping Li","raw_affiliation_strings":["Fengcheng People\u2019s Hospital, Fengcheng, China","Fengcheng People's Hospital, Fengcheng, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fengcheng People\u2019s Hospital, Fengcheng, China","institution_ids":["https://openalex.org/I4210119785","https://openalex.org/I4210147206"]},{"raw_affiliation_string":"Fengcheng People's Hospital, Fengcheng, China","institution_ids":["https://openalex.org/I4210119785"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100642952","display_name":"Zhiyong Xu","orcid":"https://orcid.org/0000-0002-3210-8839"},"institutions":[{"id":"https://openalex.org/I4210162891","display_name":"Huanggang Central Hospital","ror":"https://ror.org/02sjdcn27","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210162891"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyong Xu","raw_affiliation_strings":["Huanggang Traditional Chinese Medicine Hospital, Huanggang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huanggang Traditional Chinese Medicine Hospital, Huanggang, China","institution_ids":["https://openalex.org/I4210162891"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089534184","display_name":"Jiugen Ruan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109434","display_name":"Xinyu University","ror":"https://ror.org/021xwcd05","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210109434"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiugen Ruan","raw_affiliation_strings":["Xinyu City People\u2019s Hospital, Xinyu, China","Xinyu City People's Hospital, Xinyu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinyu City People\u2019s Hospital, Xinyu, China","institution_ids":["https://openalex.org/I4210109434"]},{"raw_affiliation_string":"Xinyu City People's Hospital, Xinyu, China","institution_ids":["https://openalex.org/I4210109434"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101657075","display_name":"Haifeng Zhu","orcid":"https://orcid.org/0000-0001-6401-6448"},"institutions":[{"id":"https://openalex.org/I4210161699","display_name":"Aviation General Hospital","ror":"https://ror.org/04j1qx617","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210161699"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haifeng Zhu","raw_affiliation_strings":["Civil Aviation General Hospital, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Civil Aviation General Hospital, Beijing, China","institution_ids":["https://openalex.org/I4210161699"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010776452","display_name":"Tao Meng","orcid":"https://orcid.org/0000-0002-7390-3601"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao Meng","raw_affiliation_strings":["RIMAG Medical Imaging Corporation, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIMAG Medical Imaging Corporation, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100456986","display_name":"Kang Li","orcid":"https://orcid.org/0000-0002-8136-9816"},"institutions":[{"id":"https://openalex.org/I2800091995","display_name":"West China Medical Center of Sichuan University","ror":"https://ror.org/040nggs60","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I24185976","https://openalex.org/I2800091995"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kang Li","raw_affiliation_strings":["West China Biomedical Big Data Center, Sichuan University, West China Hospital, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"West China Biomedical Big Data Center, Sichuan University, West China Hospital, Chengdu, China","institution_ids":["https://openalex.org/I2800091995"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101456488","display_name":"Ning Huang","orcid":"https://orcid.org/0000-0002-4639-2041"},"institutions":[{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Huang","raw_affiliation_strings":["SenseTime Research, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SenseTime Research, Beijing, China","institution_ids":["https://openalex.org/I4210128910"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066553616","display_name":"Shaoting Zhang","orcid":"https://orcid.org/0000-0002-8719-448X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I4210128910","display_name":"Group Sense (China)","ror":"https://ror.org/036wd5777","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210128910"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaoting Zhang","raw_affiliation_strings":["School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China","SenseTime Research, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"SenseTime Research, Shanghai, China","institution_ids":["https://openalex.org/I4210128910"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":36.907,"has_fulltext":false,"cited_by_count":478,"citation_normalized_percentile":{"value":0.9979271,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"39","issue":"8","first_page":"2653","last_page":"2663"},"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":0.983299970626831,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9781000018119812,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/segmentation","display_name":"Segmentation","score":0.7583512663841248},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7411827445030212},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7292410135269165},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.7045996189117432},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6696903705596924},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6543202996253967},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.5286785960197449},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5211275219917297},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4894045889377594},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3799758553504944},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36141735315322876},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2006731629371643},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12407135963439941},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0736125111579895}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7583512663841248},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7411827445030212},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7292410135269165},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.7045996189117432},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6696903705596924},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6543202996253967},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.5286785960197449},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5211275219917297},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4894045889377594},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3799758553504944},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36141735315322876},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2006731629371643},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12407135963439941},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0736125111579895},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000073640","descriptor_name":"Betacoronavirus","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000073640","descriptor_name":"Betacoronavirus","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000073640","descriptor_name":"Betacoronavirus","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000086382","descriptor_name":"COVID-19","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000086382","descriptor_name":"COVID-19","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000086382","descriptor_name":"COVID-19","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000086402","descriptor_name":"SARS-CoV-2","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000086402","descriptor_name":"SARS-CoV-2","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000086402","descriptor_name":"SARS-CoV-2","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008168","descriptor_name":"Lung","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D008168","descriptor_name":"Lung","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D008168","descriptor_name":"Lung","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D011024","descriptor_name":"Pneumonia, Viral","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D011024","descriptor_name":"Pneumonia, Viral","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D011024","descriptor_name":"Pneumonia, Viral","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D018352","descriptor_name":"Coronavirus Infections","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D018352","descriptor_name":"Coronavirus Infections","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D018352","descriptor_name":"Coronavirus Infections","qualifier_ui":"Q000000981","qualifier_name":"diagnostic imaging","is_major_topic":false},{"descriptor_ui":"D058873","descriptor_name":"Pandemics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D058873","descriptor_name":"Pandemics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D058873","descriptor_name":"Pandemics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1109/tmi.2020.3000314","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2020.3000314","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:32730215","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32730215","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on medical imaging","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:8544954","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8544954","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Trans Med Imaging","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:8544954","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8544954","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Trans Med Imaging","raw_type":"Text"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.699999988079071}],"awards":[{"id":"https://openalex.org/G6681319718","display_name":null,"funder_award_id":"81771921","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G817963173","display_name":null,"funder_award_id":"61901084","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":78,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2194775991","https://openalex.org/W2464708700","https://openalex.org/W2533800772","https://openalex.org/W2592691248","https://openalex.org/W2752782242","https://openalex.org/W2795282075","https://openalex.org/W2798122215","https://openalex.org/W2803187616","https://openalex.org/W2884436604","https://openalex.org/W2884561390","https://openalex.org/W2888493720","https://openalex.org/W2889640697","https://openalex.org/W2890123008","https://openalex.org/W2893299523","https://openalex.org/W2898352483","https://openalex.org/W2904856451","https://openalex.org/W2912934043","https://openalex.org/W2912989244","https://openalex.org/W2914448714","https://openalex.org/W2922064308","https://openalex.org/W2953070460","https://openalex.org/W2962772649","https://openalex.org/W2962914239","https://openalex.org/W2963262394","https://openalex.org/W2963351448","https://openalex.org/W2963371670","https://openalex.org/W2963420686","https://openalex.org/W2963449430","https://openalex.org/W2963697299","https://openalex.org/W2963717741","https://openalex.org/W2963759070","https://openalex.org/W2964184998","https://openalex.org/W2964309882","https://openalex.org/W2979447087","https://openalex.org/W2979901318","https://openalex.org/W2979907638","https://openalex.org/W2982361422","https://openalex.org/W2985276900","https://openalex.org/W2995624272","https://openalex.org/W3001897055","https://openalex.org/W3003901880","https://openalex.org/W3006082171","https://openalex.org/W3006367816","https://openalex.org/W3010313912","https://openalex.org/W3011041531","https://openalex.org/W3011149445","https://openalex.org/W3012602559","https://openalex.org/W3012916860","https://openalex.org/W3013633552","https://openalex.org/W3014725478","https://openalex.org/W3016684040","https://openalex.org/W3036586801","https://openalex.org/W3096956107","https://openalex.org/W3098414035","https://openalex.org/W3102469298","https://openalex.org/W3104810384","https://openalex.org/W4288095202","https://openalex.org/W6639824700","https://openalex.org/W6733814495","https://openalex.org/W6736098614","https://openalex.org/W6745986955","https://openalex.org/W6750469568","https://openalex.org/W6750523955","https://openalex.org/W6751420435","https://openalex.org/W6753182481","https://openalex.org/W6754606571","https://openalex.org/W6754684794","https://openalex.org/W6759215631","https://openalex.org/W6761059766","https://openalex.org/W6767623350","https://openalex.org/W6768848008","https://openalex.org/W6771099120","https://openalex.org/W6774662769","https://openalex.org/W6774807847","https://openalex.org/W6775116754","https://openalex.org/W6775868955","https://openalex.org/W6784761720"],"related_works":["https://openalex.org/W4402926319","https://openalex.org/W4389060404","https://openalex.org/W2973136608","https://openalex.org/W3012828488","https://openalex.org/W4286233748","https://openalex.org/W4254054209","https://openalex.org/W3197954266","https://openalex.org/W4389009345","https://openalex.org/W4200334192","https://openalex.org/W3047746737"],"abstract_inverted_index":{"Segmentation":[0],"of":[1,7,31,85,142,160,239],"pneumonia":[2,246],"lesions":[3,115],"from":[4,40,67,181,241],"CT":[5],"scans":[6],"COVID-19":[8,104,245],"patients":[9],"is":[10,82,146,153,176],"important":[11],"for":[12,70,88,96,134,244],"accurate":[13],"diagnosis":[14],"and":[15,90,119,125,217,230],"follow-up.":[16],"Deep":[17],"learning":[18,180,240],"has":[19,49,168],"a":[20,28,50,61,77,83,102,143,149,169,226],"potential":[21,51],"to":[22,37,47,52,65,110,163],"automate":[23],"this":[24,54,57],"task":[25],"but":[26],"requires":[27],"large":[29,170],"set":[30],"high-quality":[32],"annotations":[33],"that":[34,44,81,152],"are":[35,45,127],"difficult":[36],"collect.":[38],"Learning":[39],"noisy":[41,68,242],"training":[42,171,228,234],"labels":[43,69,243],"easier":[46],"obtain":[48],"alleviate":[53],"problem.":[55],"To":[56],"end,":[58],"we":[59],"propose":[60,101],"novel":[62,103],"noise-robust":[63,78,122,197,202,233],"framework":[64,133,220],"learn":[66],"the":[71,114,158,161,166,182,186,189,206,237],"segmentation":[72,89,107,215],"task.":[73],"We":[74],"first":[75],"introduce":[76],"Dice":[79,86,123,198],"loss":[80,87,95,124,199,203],"generalization":[84],"Mean":[91],"Absolute":[92],"Error":[93],"(MAE)":[94],"robustness":[97],"against":[98],"noise,":[99],"then":[100],"Pneumonia":[105],"Lesion":[106],"network":[108],"(COPLE-Net)":[109],"better":[111],"deal":[112],"with":[113,116,129,221],"various":[117],"scales":[118],"appearances.":[120],"The":[121,173],"COPLE-Net":[126,208],"combined":[128],"an":[130,137],"adaptive":[131,178,222],"self-ensembling":[132,223],"training,":[135],"where":[136],"Exponential":[138],"Moving":[139],"Average":[140],"(EMA)":[141],"student":[144,162,167,174],"model":[145,151,175],"used":[147],"as":[148],"teacher":[150,183,187],"adaptively":[154],"updated":[155],"by":[156,179],"suppressing":[157],"contribution":[159],"EMA":[164],"when":[165,185],"loss.":[172],"also":[177],"only":[184],"outperforms":[188,200,225],"student.":[190],"Experimental":[191],"results":[192],"showed":[193],"that:":[194],"(1)":[195],"our":[196,219],"existing":[201],"functions,":[204],"(2)":[205],"proposed":[207],"achieves":[209],"higher":[210],"performance":[211],"than":[212],"state-of-the-art":[213],"image":[214],"networks,":[216],"(3)":[218],"significantly":[224],"standard":[227],"process":[229],"surpasses":[231],"other":[232],"approaches":[235],"in":[236],"scenario":[238],"lesion":[247],"segmentation.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":15},{"year":2025,"cited_by_count":36},{"year":2024,"cited_by_count":69},{"year":2023,"cited_by_count":89},{"year":2022,"cited_by_count":113},{"year":2021,"cited_by_count":130},{"year":2020,"cited_by_count":26}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2020-06-12T00:00:00"}
