{"id":"https://openalex.org/W7148519175","doi":"https://doi.org/10.48550/arxiv.2604.01038","title":"Foundation Model-guided Iteratively Prompting and Pseudo-Labeling for Partially Labeled Medical Image Segmentation","display_name":"Foundation Model-guided Iteratively Prompting and Pseudo-Labeling for Partially Labeled Medical Image Segmentation","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148519175","doi":"https://doi.org/10.48550/arxiv.2604.01038"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.01038","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01038","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.2604.01038","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132798841","display_name":"Qiaochu Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Qiaochu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132790148","display_name":"Wei Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049525091","display_name":"David P. Horowitz","orcid":"https://orcid.org/0000-0002-4481-3770"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Horowitz, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132811681","display_name":"Richard Bakst","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bakst, Richard","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5087485509","display_name":"Yading Yuan","orcid":"https://orcid.org/0000-0002-4483-6702"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Yading","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":0.4925000071525574,"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":0.4925000071525574,"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":0.1264999955892563,"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/T10358","display_name":"Advanced Radiotherapy Techniques","score":0.09239999949932098,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7695000171661377},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5985999703407288},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5899999737739563},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4909999966621399},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4535999894142151},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4447000026702881}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7695000171661377},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.722599983215332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.623199999332428},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5985999703407288},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5899999737739563},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48910000920295715},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4535999894142151},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4447000026702881},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.38929998874664307},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.34619998931884766},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.28870001435279846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.01038","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01038","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.2604.01038","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01038","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":[{"score":0.4635440409183502,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automated":[0],"medical":[1,58],"image":[2,59],"segmentation":[3,75,101],"has":[4],"achieved":[5],"remarkable":[6],"progress":[7],"with":[8,26,93],"fully":[9,112],"labeled":[10,37,57,113,122],"data.":[11],"However,":[12],"site-specific":[13],"clinical":[14,135],"priorities":[15],"and":[16,52,64,78,106,129],"the":[17,35,89,94,108,111],"high":[18],"cost":[19],"of":[20,30,110,124],"manual":[21],"annotation":[22],"often":[23],"yield":[24],"scans":[25],"only":[27],"a":[28,73,79,119],"subset":[29],"organs":[31,69],"labeled,":[32],"leading":[33],"to":[34],"partially":[36,56,121],"problem":[38],"that":[39],"degrades":[40],"performance.":[41],"To":[42],"address":[43],"this":[44],"issue,":[45],"we":[46],"propose":[47],"IPnP,":[48],"an":[49],"Iteratively":[50],"Prompting":[51],"Pseudo-labeling":[53],"framework,":[54],"for":[55,67],"segmentation.":[60],"IPnP":[61,98],"iteratively":[62],"generates":[63],"refines":[65],"pseudo-labels":[66],"unlabeled":[68],"through":[70],"collaboration":[71],"between":[72],"trainable":[74],"network":[76],"(specialist)":[77],"frozen":[80],"foundation":[81],"model":[82],"(generalist),":[83],"progressively":[84],"recovering":[85],"full-organ":[86],"supervision.":[87],"On":[88],"public":[90],"dataset":[91,123],"AMOS":[92],"simulated":[95],"partial-label":[96],"setting,":[97],"consistently":[99],"improves":[100],"performance":[102,109],"over":[103],"prior":[104],"methods":[105],"approaches":[107],"reference.":[114],"We":[115],"further":[116],"evaluate":[117],"on":[118],"private,":[120],"210":[125],"head-and-neck":[126],"cancer":[127],"patients":[128],"demonstrate":[130],"our":[131],"effectiveness":[132],"in":[133],"real-world":[134],"settings.":[136]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-03T00:00:00"}