{"id":"https://openalex.org/W4390971255","doi":"https://doi.org/10.1109/bibm58861.2023.10385966","title":"Semi-supervised Medical Image Segmentation via Feature-perturbed Consistency","display_name":"Semi-supervised Medical Image Segmentation via Feature-perturbed Consistency","publication_year":2023,"publication_date":"2023-12-05","ids":{"openalex":"https://openalex.org/W4390971255","doi":"https://doi.org/10.1109/bibm58861.2023.10385966"},"language":"en","primary_location":{"id":"doi:10.1109/bibm58861.2023.10385966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm58861.2023.10385966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","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/A5100397616","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0002-5070-4511"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Yang","raw_affiliation_strings":["Harbin Institute of Technology,School of Computer Science and Technology,Shenzhen,China","School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology,School of Computer Science and Technology,Shenzhen,China","institution_ids":["https://openalex.org/I204983213"]},{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100707149","display_name":"Ruixuan Wang","orcid":"https://orcid.org/0000-0002-8714-0369"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]},{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruixuan Wang","raw_affiliation_strings":["Peng Cheng Laboratory,Department of Network Intelligence,Shenzhen,China","School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","Department of Network Intelligence, Peng Cheng Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory,Department of Network Intelligence,Shenzhen,China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Department of Network Intelligence, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100378774","display_name":"Tong Zhang","orcid":"https://orcid.org/0000-0002-0683-4669"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tong Zhang","raw_affiliation_strings":["Peng Cheng Laboratory,Department of Network Intelligence,Shenzhen,China","Department of Network Intelligence, Peng Cheng Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Peng Cheng Laboratory,Department of Network Intelligence,Shenzhen,China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Department of Network Intelligence, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069706913","display_name":"Jingyong Su","orcid":"https://orcid.org/0000-0003-3216-7027"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyong Su","raw_affiliation_strings":["Harbin Institute of Technology,School of Computer Science and Technology,Shenzhen,China","School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology,School of Computer Science and Technology,Shenzhen,China","institution_ids":["https://openalex.org/I204983213"]},{"raw_affiliation_string":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100397616"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":0.7379,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74032051,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1635","last_page":"1642"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9987999796867371,"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":0.7650750875473022},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6835838556289673},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6818829774856567},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6081000566482544},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5572296380996704},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5287266373634338},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49102163314819336},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47129666805267334},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.45770761370658875},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.44618552923202515},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41247978806495667},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36903613805770874}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7650750875473022},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6835838556289673},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6818829774856567},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6081000566482544},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5572296380996704},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5287266373634338},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49102163314819336},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47129666805267334},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.45770761370658875},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.44618552923202515},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41247978806495667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36903613805770874},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm58861.2023.10385966","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm58861.2023.10385966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.4000000059604645}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336569","display_name":"Shenzhen Science and Technology Innovation Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1641498739","https://openalex.org/W1936750108","https://openalex.org/W2804047627","https://openalex.org/W2953070460","https://openalex.org/W2979433110","https://openalex.org/W2979632994","https://openalex.org/W2979907638","https://openalex.org/W3014974815","https://openalex.org/W3031923721","https://openalex.org/W3035680157","https://openalex.org/W3044738063","https://openalex.org/W3083779147","https://openalex.org/W3090844657","https://openalex.org/W3091304180","https://openalex.org/W3095848620","https://openalex.org/W3097499499","https://openalex.org/W3112701542","https://openalex.org/W3162410350","https://openalex.org/W3171581326","https://openalex.org/W3202868380","https://openalex.org/W3203488660","https://openalex.org/W3204025806","https://openalex.org/W3204221214","https://openalex.org/W3206985657","https://openalex.org/W4225827210","https://openalex.org/W4281629935","https://openalex.org/W4282935002","https://openalex.org/W4285040393","https://openalex.org/W4287755086","https://openalex.org/W4297094919","https://openalex.org/W4310595042","https://openalex.org/W4313137950","https://openalex.org/W4313413305","https://openalex.org/W4313525603","https://openalex.org/W4323924189","https://openalex.org/W4385076661","https://openalex.org/W4386065420","https://openalex.org/W4386071539","https://openalex.org/W4386075588","https://openalex.org/W6733814495","https://openalex.org/W6779992872","https://openalex.org/W6788329692"],"related_works":["https://openalex.org/W3082178636","https://openalex.org/W2782041652","https://openalex.org/W2612657834","https://openalex.org/W2392157706","https://openalex.org/W2599192953","https://openalex.org/W2952088488","https://openalex.org/W1521968289","https://openalex.org/W4225691210","https://openalex.org/W4399568863","https://openalex.org/W2792147139"],"abstract_inverted_index":{"Although":[0],"deep":[1],"convolutional":[2],"neural":[3],"networks":[4],"have":[5],"achieved":[6],"satisfactory":[7],"performance":[8],"in":[9,76,199],"many":[10],"medical":[11],"image":[12],"segmentation":[13,60,88,108,201],"tasks,":[14],"a":[15,57,83,94],"considerable":[16],"annotation":[17],"challenge":[18],"still":[19],"needs":[20],"to":[21,69,106,120,133,161,168],"be":[22,125,134],"solved,":[23],"which":[24,123],"is":[25,207],"expensive":[26],"and":[27,97,127,142,152,178,186],"time-consuming":[28],"for":[29,165],"radiologists.":[30],"Most":[31],"existing":[32],"popular":[33],"semi-supervised":[34,59],"methods":[35],"mainly":[36],"impose":[37],"data-level":[38],"perturbations":[39,45,64],"(e.g.,":[40,46],"rotation,":[41],"noising)":[42],"or":[43],"feature-level":[44,119],"MC":[47],"dropout)":[48],"on":[49,173],"unlabeled":[50,78],"data.":[51,79],"In":[52,191],"this":[53],"paper,":[54],"we":[55,81,156],"propose":[56,158],"novel":[58],"strategy":[61],"with":[62,93],"meaningful":[63],"at":[65,117,136,209],"the":[66,77,87,98,103,115,118,154,174,179,184,193,200],"feature":[67],"level":[68],"leverage":[70],"abundant":[71],"useful":[72],"information":[73,105],"naturally":[74],"embedded":[75],"Specifically,":[80],"develop":[82],"dual-task":[84],"network":[85,116],"where":[86],"head":[89,100],"produces":[90],"multiple":[91],"predictions":[92,122,151],"perturbation":[95],"module,":[96],"reconstruction":[99],"further":[101,157],"utilizes":[102],"semantic":[104],"enhance":[107],"performance.":[109],"The":[110,205],"proposed":[111,194],"framework":[112,167],"subtly":[113],"perturbs":[114],"generate":[121],"should":[124],"similar":[126],"consistent.":[128],"However,":[129],"enforcing":[130],"them":[131],"roughly":[132],"consistent":[135],"all":[137],"pixels":[138],"harms":[139],"stable":[140],"training":[141],"neglects":[143],"much":[144],"delicate":[145],"information.":[146],"To":[147],"better":[148],"utilize":[149],"those":[150],"estimate":[153],"uncertainty,":[155],"feature-perturbed":[159],"consistency":[160],"exploit":[162],"reliable":[163],"regions":[164],"our":[166,189],"learn":[169],"from.":[170],"Extensive":[171],"experiments":[172],"public":[175],"BraTS2020":[176],"dataset":[177,182],"2017":[180],"ACDC":[181],"confirm":[183],"efficiency":[185],"effectiveness":[187],"of":[188,202],"method.":[190],"particular,":[192],"method":[195],"demonstrates":[196],"remarkable":[197],"superiority":[198],"boundary":[203],"regions.":[204],"project":[206],"available":[208],"https://github.com/youngyzzZ/SFPC.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}