{"id":"https://openalex.org/W4406238066","doi":"https://doi.org/10.1109/bibm62325.2024.10822398","title":"UP-SAM: Uncertainty-Informed Adaptation of Segment Anything Model for Semi-Supervised Medical Image Segmentation","display_name":"UP-SAM: Uncertainty-Informed Adaptation of Segment Anything Model for Semi-Supervised Medical Image Segmentation","publication_year":2024,"publication_date":"2024-12-03","ids":{"openalex":"https://openalex.org/W4406238066","doi":"https://doi.org/10.1109/bibm62325.2024.10822398"},"language":"en","primary_location":{"id":"doi:10.1109/bibm62325.2024.10822398","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10822398","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5101990722","display_name":"Wenjing Lu","orcid":"https://orcid.org/0000-0002-9987-5549"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjing Lu","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Computer Science and Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Computer Science and Engineering,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101091381","display_name":"Yi Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Hong","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Computer Science and Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Computer Science and Engineering,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101420003","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0001-8711-0437"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yang","raw_affiliation_strings":["Shanghai Jiao Tong University,Department of Computer Science and Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Department of Computer Science and Engineering,China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":4.15,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.94822382,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2256","last_page":"2261"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9027000069618225,"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":0.9027000069618225,"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/adaptation","display_name":"Adaptation (eye)","score":0.7144644260406494},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6944032311439514},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6875863671302795},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6845235824584961},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6239845752716064},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5798929929733276},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5084656476974487},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1020386815071106}],"concepts":[{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.7144644260406494},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6944032311439514},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6875863671302795},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6845235824584961},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6239845752716064},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5798929929733276},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5084656476974487},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1020386815071106},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm62325.2024.10822398","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10822398","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W855272188","https://openalex.org/W2962914239","https://openalex.org/W2963351448","https://openalex.org/W2979907638","https://openalex.org/W3014596384","https://openalex.org/W3031923721","https://openalex.org/W3044738063","https://openalex.org/W3090844657","https://openalex.org/W3093394156","https://openalex.org/W3194662286","https://openalex.org/W3197217317","https://openalex.org/W3202868380","https://openalex.org/W3203046931","https://openalex.org/W4221161877","https://openalex.org/W4282935002","https://openalex.org/W4285040393","https://openalex.org/W4296119796","https://openalex.org/W4310466409","https://openalex.org/W4382404966","https://openalex.org/W4386075941","https://openalex.org/W4387097385","https://openalex.org/W4387211216","https://openalex.org/W4389430914","https://openalex.org/W4390874575","https://openalex.org/W4391109864","https://openalex.org/W4401750097","https://openalex.org/W4401752224","https://openalex.org/W4401819803","https://openalex.org/W4404801501","https://openalex.org/W6733814495","https://openalex.org/W6752558437","https://openalex.org/W6779384589","https://openalex.org/W6851673123","https://openalex.org/W6851980744","https://openalex.org/W6857385713","https://openalex.org/W6859110375"],"related_works":["https://openalex.org/W2997567050","https://openalex.org/W1483272040","https://openalex.org/W4283377908","https://openalex.org/W1526712007","https://openalex.org/W1533421371","https://openalex.org/W2003050223","https://openalex.org/W2091777911","https://openalex.org/W2766405861","https://openalex.org/W2360975119","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Semi-supervised":[0],"segmentation":[1,97,116],"is":[2,28,103,187],"extensively":[3],"employed":[4],"in":[5,144],"medical":[6,114],"image":[7,115],"analysis":[8],"due":[9],"to":[10,13,89,140],"its":[11,26],"ability":[12],"leverage":[14],"a":[15,71,75,111,141],"small":[16],"amount":[17],"of":[18,34,46,95,147,164],"labeled":[19,41,184],"data":[20,36,42],"alongside":[21],"abundant":[22],"unlabeled":[23,51],"data.":[24,185],"However,":[25],"performance":[27],"hindered":[29],"by":[30,79],"the":[31,35,44,63,91,145,154,161],"inadequate":[32],"knowledge":[33],"domain":[37,83],"learned":[38],"from":[39],"limited":[40],"and":[43,81,136,149,157],"absence":[45],"effective":[47,130],"strategies":[48],"for":[49,123],"exploiting":[50],"regions,":[52],"especially":[53],"when":[54,179],"annotations":[55],"are":[56],"extremely":[57],"scarce.":[58],"To":[59],"address":[60],"these":[61],"challenges,":[62],"Segment":[64],"Anything":[65],"Model":[66],"(SAM)":[67],"has":[68,86],"emerged":[69],"as":[70],"promising":[72],"solution.":[73],"As":[74],"foundation":[76,134],"model":[77],"enriched":[78],"extensive":[80],"diverse":[82],"knowledge,":[84],"SAM":[85,122],"been":[87],"leveraged":[88],"mitigate":[90],"epistemic":[92],"uncertainty":[93,101,125],"(EU)":[94],"semi-supervised":[96,113,176],"models,":[98,138],"while":[99],"aleatoric":[100],"(AU)":[102],"often":[104],"ignored.":[105],"In":[106],"this":[107],"paper,":[108],"we":[109],"propose":[110],"novel":[112],"framework":[117,128],"called":[118],"UP-SAM,":[119],"which":[120],"adapts":[121],"dual":[124],"assessments.":[126],"The":[127,151],"achieves":[129],"collaboration":[131],"between":[132],"large":[133],"models":[135,178],"domain-specific":[137],"leading":[139],"simultaneous":[142],"reduction":[143],"impact":[146],"EU":[148],"AU.":[150],"experiments":[152],"on":[153],"left":[155],"atrium":[156],"pancreas":[158],"datasets":[159],"demonstrate":[160],"superior":[162],"efficacy":[163],"UP-SAM":[165,170],"against":[166],"baseline":[167],"methods.":[168],"Particularly,":[169],"exhibits":[171],"substantial":[172],"advantages":[173],"over":[174],"other":[175],"learning":[177],"dealing":[180],"with":[181],"exceedingly":[182],"scarce":[183],"Code":[186],"available":[188],"at":[189],"https://github.com/VivienLu/UP-SAM.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
