{"id":"https://openalex.org/W7160612040","doi":"https://doi.org/10.48550/arxiv.2605.05979","title":"Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapters","display_name":"Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapters","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160612040","doi":"https://doi.org/10.48550/arxiv.2605.05979"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.05979","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05979","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.05979","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107404759","display_name":"Hinako Mitsuoka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mitsuoka, Hinako","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135647647","display_name":"Kazuhiro Hotta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hotta, Kazuhiro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.42250001430511475,"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":0.42250001430511475,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.23499999940395355,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.11789999902248383,"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/segmentation","display_name":"Segmentation","score":0.7950000166893005},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.6588000059127808},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.529699981212616},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4787999987602234},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.47850000858306885},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.478300005197525},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.46380001306533813}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8033999800682068},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7950000166893005},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.6588000059127808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6421999931335449},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.529699981212616},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4787999987602234},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.47850000858306885},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.478300005197525},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4593999981880188},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.44530001282691956},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4381999969482422},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4187000095844269},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3637999892234802},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3467000126838684},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.05979","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05979","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.05979","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05979","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Segment":[0],"Anything":[1],"Model":[2],"2":[3],"(SAM2)":[4],"demonstrated":[5],"impressive":[6],"zero-shot":[7],"capabilities":[8],"on":[9,41,83],"natural":[10],"images":[11],"but":[12],"faces":[13],"challenges":[14],"in":[15],"biomedical":[16],"segmentation":[17,40,104],"due":[18],"to":[19,51,54,78,108,122],"significant":[20],"domain":[21],"shifts":[22],"and":[23,58,72,88,133],"prompt":[24],"dependency.":[25],"To":[26],"address":[27],"these":[28],"limitations,":[29],"we":[30],"propose":[31],"a":[32,46,60,128],"prompt-free,":[33],"parameter-efficient":[34],"fine-tuning":[35],"framework":[36],"designed":[37],"for":[38,68],"multi-class":[39],"variable-sized":[42],"inputs.":[43],"We":[44],"introduce":[45],"convolutional":[47],"Positional":[48],"Encoding":[49],"Generator":[50],"adapt":[52],"effectively":[53],"arbitrary":[55],"aspect":[56],"ratios":[57],"present":[59],"dual-adapter":[61],"strategy:":[62],"High-Performance":[63],"Adapter":[64,74],"utilizing":[65],"deformable":[66],"convolutions":[67],"precise":[69],"boundary":[70],"modeling":[71],"Lightweight":[73],"employing":[75],"structural":[76],"re-parameterization":[77],"minimize":[79],"inference":[80],"latency.":[81],"Experiments":[82],"ISBI":[84],"2012,":[85],"Kvasir-SEG,":[86],"Synapse,":[87],"ACDC":[89],"datasets":[90],"demonstrate":[91],"that":[92],"our":[93,101],"approach":[94],"significantly":[95],"outperforms":[96],"strong":[97],"adaptation":[98],"baselines.":[99],"Specifically,":[100],"method":[102],"improved":[103],"accuracy":[105,132],"by":[106,118],"up":[107],"19.66\\%":[109],"over":[110],"the":[111],"vanilla":[112],"SAM2,":[113],"while":[114],"reducing":[115],"computational":[116],"costs":[117],"approximately":[119],"87\\%":[120],"compared":[121],"heavyweight":[123],"medical":[124],"SAM":[125],"adaptations,":[126],"establishing":[127],"superior":[129],"trade-off":[130],"between":[131],"efficiency.":[134]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-09T00:00:00"}