{"id":"https://openalex.org/W4406261106","doi":"https://doi.org/10.1109/bibm62325.2024.10822087","title":"BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Models","display_name":"BiSeg-SAM: Weakly-Supervised Post-Processing Framework for Boosting Binary Segmentation in Segment Anything Models","publication_year":2024,"publication_date":"2024-12-03","ids":{"openalex":"https://openalex.org/W4406261106","doi":"https://doi.org/10.1109/bibm62325.2024.10822087"},"language":"en","primary_location":{"id":"doi:10.1109/bibm62325.2024.10822087","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10822087","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":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2504.01452","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070995732","display_name":"Eileen Lee Ming Su","orcid":"https://orcid.org/0000-0001-9366-5404"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Encheng Su","raw_affiliation_strings":["Technische Universität München,Engineering and Design,München,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universität München,Engineering and Design,München,Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011193488","display_name":"Hu Cao","orcid":"https://orcid.org/0000-0001-8225-858X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu Cao","raw_affiliation_strings":["Technische Universität München,Computation, Information and Technology,München,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universität München,Computation, Information and Technology,München,Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063781430","display_name":"Alois Knoll","orcid":"https://orcid.org/0000-0003-4840-076X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alois Knoll","raw_affiliation_strings":["Technische Universität München,Computation, Information and Technology,München,Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universität München,Computation, Information and Technology,München,Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5070995732"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4461,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.88070994,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2430","last_page":"2437"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.8289999961853027,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.8289999961853027,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.7477999925613403,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.7421000003814697,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.8563416004180908},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6850801706314087},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6843429207801819},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.597943127155304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5919031500816345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4799240231513977},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4585871398448944},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16016951203346252}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.8563416004180908},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6850801706314087},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6843429207801819},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.597943127155304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5919031500816345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4799240231513977},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4585871398448944},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16016951203346252},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bibm62325.2024.10822087","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10822087","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"},{"id":"pmh:oai:arXiv.org:2504.01452","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.01452","pdf_url":"https://arxiv.org/pdf/2504.01452","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2504.01452","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.01452","pdf_url":"https://arxiv.org/pdf/2504.01452","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2008359794","https://openalex.org/W2021088830","https://openalex.org/W2128079945","https://openalex.org/W2194775991","https://openalex.org/W2285968993","https://openalex.org/W2560328367","https://openalex.org/W2884436604","https://openalex.org/W2961348656","https://openalex.org/W2997286550","https://openalex.org/W3092344722","https://openalex.org/W3160284783","https://openalex.org/W3177634011","https://openalex.org/W3196075503","https://openalex.org/W3202263958","https://openalex.org/W3204166336","https://openalex.org/W4213019189","https://openalex.org/W4313527340","https://openalex.org/W4382877880","https://openalex.org/W4387211576","https://openalex.org/W4387886152","https://openalex.org/W4390874575","https://openalex.org/W4390971106","https://openalex.org/W4393571644","https://openalex.org/W4400275685","https://openalex.org/W4402753647","https://openalex.org/W4404801501","https://openalex.org/W6734824979","https://openalex.org/W6811477674","https://openalex.org/W6855436210"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W2147697413","https://openalex.org/W2154063878","https://openalex.org/W4231274751","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1538046993","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Accurate":[0],"segmentation":[1,17,65,96,169,192],"of":[2,36,75,97,179],"polyps":[3,20,98],"and":[4,12,21,44,90,99,130,148,168,209,221],"skin":[5,13,22,100,211],"lesions":[6,23],"is":[7,41,219],"essential":[8],"for":[9,19,94,126,138,158,216],"diagnosing":[10],"colorectal":[11],"cancers.":[14],"While":[15],"various":[16],"methods":[18,202],"using":[24,131,175],"fully":[25],"supervised":[26,88],"deep":[27],"learning":[28],"techniques":[29],"have":[30,55],"been":[31],"developed,":[32],"the":[33,50,73,95,127,143],"pixel-level":[34],"annotation":[35],"medical":[37,64,77],"images":[38],"by":[39,171],"doctors":[40],"both":[42],"time-consuming":[43],"costly.":[45],"Foundational":[46],"vision":[47],"models":[48],"like":[49],"Segment":[51],"Anything":[52],"Model":[53],"(SAM)":[54],"demonstrated":[56],"superior":[57],"performance;":[58],"however,":[59],"directly":[60],"applying":[61],"SAM":[62,105,128],"to":[63,72,111,188],"may":[66],"not":[67],"yield":[68],"satisfactory":[69],"results":[70],"due":[71],"lack":[74],"domain-specific":[76],"knowledge.":[78],"In":[79],"this":[80,217],"paper,":[81],"we":[82,103,152],"propose":[83],"BiSeg-SAM,":[84],"a":[85,108,117,176],"SAM-guided":[86],"weakly":[87],"prompting":[89],"boundary":[91,166],"refinement":[92],"network":[93],"lesions.":[101],"Specifically,":[102],"fine-tune":[104],"combined":[106],"with":[107,119],"CNN":[109],"module":[110,164],"learn":[112],"local":[113],"features.":[114],"We":[115],"introduce":[116],"WeakBox":[118],"two":[120],"functions:":[121],"automatically":[122],"generating":[123],"box":[124],"prompts":[125],"model":[129],"our":[132],"proposed":[133],"Multi-choice":[134],"Mask-to-Box":[135],"(MM2B)":[136],"transformation":[137],"rough":[139],"mask-to-box":[140],"conversion,":[141],"addressing":[142],"mismatch":[144],"between":[145],"coarse":[146,173],"labels":[147],"precise":[149],"predictions.":[150],"Additionally,":[151],"apply":[153],"scale":[154,160],"consistency":[155],"(SC)":[156],"loss":[157],"prediction":[159],"alignment.":[161],"Our":[162,194],"DetailRefine":[163],"enhances":[165],"precision":[167],"accuracy":[170],"refining":[172],"predictions":[174],"limited":[177],"amount":[178],"ground":[180],"truth":[181],"labels.":[182],"This":[183],"comprehensive":[184],"approach":[185],"enables":[186],"BiSeg-SAM":[187],"achieve":[189],"excellent":[190],"multi-task":[191],"performance.":[193],"method":[195],"demonstrates":[196],"significant":[197],"superiority":[198],"over":[199],"state-of-the-art":[200],"(SOTA)":[201],"when":[203],"tested":[204],"on":[205],"five":[206],"polyp":[207],"datasets":[208],"one":[210],"cancer":[212],"dataset.":[213],"The":[214],"code":[215],"work":[218],"open-sourced":[220],"available":[222],"at":[223],"https://github.com/suencgo/BiSeg-SAM.":[224]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}