{"id":"https://openalex.org/W4410296451","doi":"https://doi.org/10.1109/isbi60581.2025.10980911","title":"PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation","display_name":"PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation","publication_year":2025,"publication_date":"2025-04-14","ids":{"openalex":"https://openalex.org/W4410296451","doi":"https://doi.org/10.1109/isbi60581.2025.10980911"},"language":"en","primary_location":{"id":"doi:10.1109/isbi60581.2025.10980911","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi60581.2025.10980911","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100312997","display_name":"Zhonghao Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhonghao Yan","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086174282","display_name":"Zijin Yin","orcid":"https://orcid.org/0000-0002-8769-9533"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijin Yin","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068670007","display_name":"Tianyu Lin","orcid":"https://orcid.org/0000-0002-4202-4970"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyu Lin","raw_affiliation_strings":["Johns Hopkins University,Baltimore,USA"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University,Baltimore,USA","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108477651","display_name":"Xiangzhu Zeng","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]},{"id":"https://openalex.org/I4210141942","display_name":"Peking University Third Hospital","ror":"https://ror.org/04wwqze12","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210141942"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangzhu Zeng","raw_affiliation_strings":["Peking University Third Hospital,Department of Radiology,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Peking University Third Hospital,Department of Radiology,Beijing,China","institution_ids":["https://openalex.org/I20231570","https://openalex.org/I4210141942"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077313814","display_name":"Kongming Liang","orcid":"https://orcid.org/0000-0002-4726-093X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kongming Liang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039812471","display_name":"Zhanyu Ma","orcid":"https://orcid.org/0000-0003-2950-2488"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanyu Ma","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100312997"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":1.319,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.8037488,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9929999709129333,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9929999709129333,"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":0.9901000261306763,"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":0.989799976348877,"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/computer-science","display_name":"Computer science","score":0.7521695494651794},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.6920461654663086},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6577595472335815},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6571770906448364},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6556257009506226},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5889031887054443},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.49296069145202637},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4592118263244629},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07306745648384094}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7521695494651794},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.6920461654663086},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6577595472335815},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6571770906448364},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6556257009506226},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5889031887054443},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.49296069145202637},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4592118263244629},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07306745648384094},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi60581.2025.10980911","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi60581.2025.10980911","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2533974245","display_name":null,"funder_award_id":"62225601,62476029,U23B2052","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7101706402","display_name":null,"funder_award_id":"L242025","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null},{"id":"https://openalex.org/F4320334978","display_name":"Beijing Nova Program","ror":"https://ror.org/034k14f91"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W4385481295","https://openalex.org/W4387602657","https://openalex.org/W4388574886","https://openalex.org/W4390874575","https://openalex.org/W4390971106","https://openalex.org/W4391109864","https://openalex.org/W4393159543","https://openalex.org/W4402715864","https://openalex.org/W4402727285","https://openalex.org/W4403152107","https://openalex.org/W4404801501","https://openalex.org/W4405822262","https://openalex.org/W6853702739","https://openalex.org/W6902762063"],"related_works":["https://openalex.org/W2497720472","https://openalex.org/W4292659306","https://openalex.org/W3044321615","https://openalex.org/W2806221744","https://openalex.org/W2326937258","https://openalex.org/W394267150","https://openalex.org/W2773965352","https://openalex.org/W4294892107","https://openalex.org/W2357748469","https://openalex.org/W2392917037"],"abstract_inverted_index":{"The":[0],"Segment":[1],"Anything":[2],"Model":[3],"(SAM)":[4],"has":[5],"demonstrated":[6],"strong":[7],"and":[8,30,66,73,90,99,111],"versatile":[9],"segmentation":[10,23],"capabilities,":[11],"along":[12],"with":[13,125],"intuitive":[14],"prompt-based":[15],"interactions.":[16],"However,":[17],"customizing":[18],"SAM":[19,128],"for":[20,101],"medical":[21],"image":[22],"requires":[24],"massive":[25],"amounts":[26],"of":[27,134],"pixel-level":[28],"annotations":[29],"precise":[31],"point-":[32],"or":[33],"box-based":[34],"prompt":[35,103],"designs.":[36],"To":[37],"address":[38],"these":[39],"challenges,":[40],"we":[41],"introduce":[42],"PGP-SAM,":[43],"a":[44,81,92,107,112],"novel":[45],"prototype-based":[46],"few-shot":[47],"tuning":[48],"approach":[49],"that":[50,86,96,117],"uses":[51],"limited":[52],"samples":[53],"to":[54,63,69],"replace":[55],"tedious":[56],"manual":[57],"prompts.":[58],"Our":[59],"key":[60],"idea":[61],"is":[62],"leverage":[64],"inter-":[65],"intra-class":[67],"prototypes":[68,98],"capture":[70],"class-specific":[71],"knowledge":[72],"relationships.":[74],"We":[75],"propose":[76],"two":[77],"main":[78],"components:":[79],"(1)":[80],"plug-and-play":[82],"contextual":[83],"modulation":[84],"module":[85],"integrates":[87],"multi-scale":[88],"information,":[89],"(2)":[91],"class-guided":[93],"cross-attention":[94],"mechanism":[95],"fuses":[97],"features":[100],"automatic":[102],"generation.":[104],"Experiments":[105],"on":[106],"public":[108],"multi-organ":[109],"dataset":[110,115],"private":[113],"ventricle":[114],"demonstrate":[116],"PGP-SAM":[118],"achieves":[119],"superior":[120],"mean":[121],"Dice":[122],"scores":[123],"compared":[124],"existing":[126],"prompt-free":[127],"variants,":[129],"while":[130],"using":[131],"only":[132],"10%":[133],"the":[135],"2D":[136],"slices.":[137]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}