{"id":"https://openalex.org/W7165689893","doi":"https://doi.org/10.48550/arxiv.2606.21913","title":"Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation","display_name":"Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation","publication_year":2026,"publication_date":"2026-06-20","ids":{"openalex":"https://openalex.org/W7165689893","doi":"https://doi.org/10.48550/arxiv.2606.21913"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.21913","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21913","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.21913","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005356006","display_name":"Q. Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125054748","display_name":"X He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Xiangjian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139177522","display_name":"Wenting Duan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duan, Wenting","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139200221","display_name":"Jiebo Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Jiebo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139194680","display_name":"Zhen Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhen","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/T12859","display_name":"Cell Image Analysis Techniques","score":0.4586000144481659,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.4586000144481659,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.16419999301433563,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.0835999995470047,"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":0.7129999995231628},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5792999863624573},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5228999853134155},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4945000112056732},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.474700003862381},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.461899995803833},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.45019999146461487},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4487999975681305},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.4253000020980835}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7799999713897705},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7129999995231628},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6647999882698059},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5792999863624573},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5228999853134155},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.474700003862381},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.461899995803833},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.45019999146461487},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4487999975681305},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4253000020980835},{"id":"https://openalex.org/C144559511","wikidata":"https://www.wikidata.org/wiki/Q2986279","display_name":"Principal (computer security)","level":2,"score":0.41280001401901245},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3815999925136566},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.36570000648498535},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.36000001430511475},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.21913","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21913","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.21913","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21913","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":"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":{"Cell":[0,107],"segmentation":[1,68],"is":[2,29,77,182],"critical":[3],"for":[4,26,64,88,96],"computational":[5,48],"pathology":[6],"and":[7,50,92,116,147],"biomedical":[8],"discovery.":[9],"While":[10],"recent":[11],"Vision":[12],"Foundation":[13],"Models":[14],"(VFMs)":[15],"have":[16],"demonstrated":[17],"remarkable":[18],"universal":[19],"feature":[20],"representations,":[21],"unlocking":[22],"their":[23],"full":[24],"potential":[25,90],"cellular":[27,98],"imaging":[28,164],"currently":[30],"bottlenecked":[31],"by":[32,141],"resource-intensive":[33],"adaptation":[34],"paradigms.":[35],"Existing":[36],"methods":[37,160],"typically":[38],"rely":[39],"on":[40,53],"fine-tuning":[41],"heavy":[42],"visual":[43,72],"encoders,":[44],"leading":[45],"to":[46],"extensive":[47],"overhead":[49],"a":[51,106,132],"dependency":[52],"large-scale":[54],"annotations.":[55],"To":[56,100],"address":[57],"this,":[58],"we":[59,104,130],"propose":[60,131],"the":[61,71],"EffiCell-Seg":[62,157],"framework":[63],"highly":[65],"efficient":[66],"cell":[67,163],"without":[69],"re-training":[70],"encoder.":[73],"Our":[74],"core":[75],"insight":[76],"that":[78,112,137,156],"pretrained":[79],"VFMs":[80],"intrinsically":[81],"encode":[82],"complementary":[83],"structural":[84,126],"priors:":[85],"global":[86],"saliency":[87,115],"localizing":[89],"cells,":[91],"local":[93],"morphological":[94,118],"patterns":[95],"delineating":[97],"structures.":[99],"harness":[101],"these":[102],"priors,":[103],"devise":[105],"Structure":[108],"Prompt":[109],"Encoder":[110],"(CSP-Encoder)":[111],"synthesizes":[113],"semantic-aware":[114],"principal":[117],"features":[119],"from":[120],"frozen":[121],"VFM":[122,178],"representations":[123],"into":[124],"explicit":[125],"prior":[127],"maps.":[128],"Moreover,":[129],"Synergistic":[133],"Mask":[134],"Decoder":[135],"(SM-Decoder)":[136],"enforces":[138],"contextual":[139],"consistency":[140],"jointly":[142],"predicting":[143],"geometric":[144],"distance":[145],"fields":[146],"semantic":[148],"maps":[149],"via":[150],"mutual":[151],"cross-guidance.":[152],"Extensive":[153],"experiments":[154],"demonstrate":[155],"outperforms":[158],"state-of-the-art":[159],"across":[161],"diverse":[162],"modalities":[165],"while":[166],"requiring":[167],"only":[168],"~5M":[169],"trainable":[170],"parameters,":[171],"over":[172],"130x":[173],"fewer":[174],"than":[175],"fully":[176],"fine-tuned":[177],"counterparts.":[179],"The":[180],"code":[181],"available":[183],"at":[184],"https://github.com/xq141839/EffiCell-Seg.":[185]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}