{"id":"https://openalex.org/W7151076583","doi":"https://doi.org/10.48550/arxiv.2604.03134","title":"SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentation","display_name":"SD-FSMIS: Adapting Stable Diffusion for Few-Shot Medical Image Segmentation","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7151076583","doi":"https://doi.org/10.48550/arxiv.2604.03134"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03134","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03134","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.2604.03134","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133027970","display_name":"Meihua Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Meihua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133004561","display_name":"Yang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101363147","display_name":"Weizhao He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Weizhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133015886","display_name":"Hu Qu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qu, Hu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123957826","display_name":"YiSong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yisong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5133027970"],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5716999769210815,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.5716999769210815,"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":0.20270000398159027,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.08190000057220459,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6571999788284302},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6427000164985657},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5695000290870667},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5317000150680542},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5109000205993652},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4894999861717224},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4666000008583069},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45660001039505005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.734000027179718},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7067000269889832},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6571999788284302},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6427000164985657},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5695000290870667},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5317000150680542},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5109000205993652},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4894999861717224},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47850000858306885},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45660001039505005},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4275999963283539},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.42570000886917114},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4081000089645386},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.352400004863739},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33869999647140503},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3330000042915344},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.26019999384880066},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03134","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03134","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.2604.03134","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03134","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Few-Shot":[0],"Medical":[1],"Image":[2],"Segmentation":[3],"(FSMIS)":[4],"aims":[5],"to":[6,81,128,168,197],"segment":[7],"novel":[8,78],"object":[9],"classes":[10],"in":[11,30,38,171,181],"medical":[12,31,202],"images":[13],"using":[14],"only":[15],"minimal":[16],"annotated":[17],"examples,":[18],"addressing":[19],"the":[20,51,84,92,129,139,149,156,189],"critical":[21],"challenges":[22],"of":[23,125,155,192],"data":[24],"scarcity":[25],"and":[26,67,111,200],"domain":[27],"shifts":[28],"prevalent":[29],"imaging.":[32],"While":[33],"Diffusion":[34,88],"Models":[35],"(DM)":[36],"excel":[37],"visual":[39,53,136],"tasks,":[40],"their":[41],"potential":[42,191],"for":[43,63,91],"FSMIS":[44,93,130],"remains":[45],"largely":[46],"unexplored.":[47],"We":[48],"propose":[49],"that":[50,147,162],"rich":[52],"priors":[54],"learned":[55],"by":[56,102],"large-scale":[57,194],"DMs":[58],"offer":[59],"a":[60,64,77,107,112,120],"powerful":[61,85,123],"foundation":[62],"more":[65,182],"robust":[66,201],"data-efficient":[68,199],"segmentation":[69],"approach.":[70],"In":[71],"this":[72],"paper,":[73],"we":[74],"introduce":[75],"SD-FSMIS,":[76],"framework":[79],"designed":[80],"effectively":[82],"adapt":[83],"pre-trained":[86],"Stable":[87],"(SD)":[89],"model":[90],"task.":[94],"Our":[95],"approach":[96],"repurposes":[97],"its":[98],"conditional":[99],"generative":[100,195],"architecture":[101],"introducing":[103],"two":[104],"key":[105],"components:":[106],"Support-Query":[108],"Interaction":[109],"(SQI)":[110],"Visual-to-Textual":[113],"Condition":[114],"Translator":[115],"(VTCT).":[116],"Specifically,":[117],"SQI":[118],"provides":[119],"straightforward":[121],"yet":[122],"means":[124],"adapting":[126,193],"SD":[127],"paradigm.":[131],"The":[132],"VTCT":[133],"module":[134],"translates":[135],"cues":[137],"from":[138],"support":[140],"set":[141],"into":[142],"an":[143],"implicit":[144],"textual":[145],"embedding":[146],"guides":[148],"diffusion":[150],"model,":[151],"enabling":[152],"precise":[153],"conditioning":[154],"generation":[157],"process.":[158],"Extensive":[159],"experiments":[160],"demonstrate":[161],"SD-FSMIS":[163],"achieves":[164],"competitive":[165],"results":[166],"compared":[167],"state-of-the-art":[169],"methods":[170],"standard":[172],"settings.":[173],"Surprisingly,":[174],"it":[175],"also":[176],"demonstrated":[177],"excellent":[178],"generalization":[179],"ability":[180],"challenging":[183],"cross-domain":[184],"scenarios.":[185],"These":[186],"findings":[187],"highlight":[188],"immense":[190],"models":[196],"advance":[198],"image":[203],"segmentation.":[204]},"counts_by_year":[],"updated_date":"2026-04-07T06:06:30.997549","created_date":"2026-04-07T00:00:00"}