{"id":"https://openalex.org/W7143352533","doi":"https://doi.org/10.48550/arxiv.2603.25945","title":"Adapting Segment Anything Model 3 for Concept-Driven Lesion Segmentation in Medical Images: An Experimental Study","display_name":"Adapting Segment Anything Model 3 for Concept-Driven Lesion Segmentation in Medical Images: An Experimental Study","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7143352533","doi":"https://doi.org/10.48550/arxiv.2603.25945"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25945","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25945","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.25945","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130938540","display_name":"Guoping Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xu, Guoping","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065713856","display_name":"Jayaram K. Udupa","orcid":"https://orcid.org/0000-0002-7361-2927"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Udupa, Jayaram K.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130984111","display_name":"Yubing Tong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tong, Yubing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130953415","display_name":"Xin Long","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Long, Xin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130984090","display_name":"Ying Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ying","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130981727","display_name":"Jie Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Jie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130940415","display_name":"Weiguo Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Weiguo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130945671","display_name":"You Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, You","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5130938540"],"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.44339999556541443,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.44339999556541443,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.1688999980688095,"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/T10862","display_name":"AI in cancer detection","score":0.1453000009059906,"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/segmentation","display_name":"Segmentation","score":0.6841999888420105},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.585099995136261},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5443000197410583},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.508899986743927},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4853000044822693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47360000014305115}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7226999998092651},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6841999888420105},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6460999846458435},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.585099995136261},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5579000115394592},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5443000197410583},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.508899986743927},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4853000044822693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47360000014305115},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4456999897956848},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.43369999527931213},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.33809998631477356},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.30720001459121704},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.27639999985694885}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25945","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25945","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.25945","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25945","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"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":{"Accurate":[0],"lesion":[1,47,71,136,150],"segmentation":[2,31],"is":[3],"essential":[4],"in":[5,32],"medical":[6,42,165],"image":[7,43,85,166],"analysis,":[8],"yet":[9],"most":[10],"existing":[11],"methods":[12],"are":[13],"designed":[14],"for":[15,39,70,161],"specific":[16],"anatomical":[17],"sites":[18],"or":[19],"imaging":[20],"modalities,":[21,89],"limiting":[22],"their":[23],"generalizability.":[24],"Recent":[25],"vision-language":[26],"foundation":[27,159],"models":[28,160,171],"enable":[29],"concept-driven":[30,146],"natural":[33],"images,":[34],"offering":[35],"a":[36,65],"promising":[37],"direction":[38],"more":[40],"flexible":[41],"analysis.":[44],"However,":[45],"concept-prompt-based":[46],"segmentation,":[48,147],"particularly":[49],"with":[50],"the":[51,155],"latest":[52],"Segment":[53],"Anything":[54],"Model":[55],"3":[56],"(SAM3),":[57],"remains":[58],"underexplored.":[59],"In":[60],"this":[61],"work,":[62],"we":[63,101],"present":[64],"systematic":[66],"evaluation":[67],"of":[68,157],"SAM3":[69,140],"segmentation.":[72,167],"We":[73,115],"assess":[74],"its":[75],"performance":[76],"using":[77],"geometric":[78],"bounding":[79],"boxes":[80],"and":[81,84,96,112,127,148,163,169],"concept-based":[82,158],"text":[83],"prompts":[86],"across":[87],"multiple":[88],"including":[90,121],"multiparametric":[91,110],"MRI,":[92],"CT,":[93],"ultrasound,":[94],"dermoscopy,":[95],"endoscopy.":[97],"To":[98],"improve":[99],"robustness,":[100],"incorporate":[102],"additional":[103],"prior":[104,113],"knowledge,":[105],"such":[106],"as":[107],"adjacent-slice":[108],"predictions,":[109],"information,":[111],"annotations.":[114],"further":[116],"compare":[117],"different":[118],"fine-tuning":[119],"strategies,":[120],"partial":[122],"module":[123],"tuning,":[124],"adapter-based":[125],"methods,":[126],"full-model":[128],"optimization.":[129],"Experiments":[130],"on":[131],"13":[132],"datasets":[133],"covering":[134],"11":[135],"types":[137],"demonstrate":[138],"that":[139],"achieves":[141],"strong":[142],"cross-modality":[143],"generalization,":[144],"reliable":[145],"accurate":[149],"delineation.":[151],"These":[152],"results":[153],"highlight":[154],"potential":[156],"scalable":[162],"practical":[164],"Code":[168],"trained":[170],"will":[172],"be":[173],"released":[174],"at:":[175],"https://github.com/apple1986/lesion-sam3":[176]},"counts_by_year":[],"updated_date":"2026-03-31T06:07:48.031334","created_date":"2026-03-31T00:00:00"}
