{"id":"https://openalex.org/W7126081205","doi":"https://doi.org/10.1109/bibm66473.2025.11356305","title":"Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology","display_name":"Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126081205","doi":"https://doi.org/10.1109/bibm66473.2025.11356305"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356305","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356305","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5124193488","display_name":"Yuxuan Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxuan Chen","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100331436","display_name":"Jiawen Li","orcid":"https://orcid.org/0000-0001-9339-2557"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawen Li","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124234550","display_name":"Jiali Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiali Hu","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111335051","display_name":"Xitong Ling","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xitong Ling","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124184513","display_name":"Tian Guan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Guan","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Anjia Han","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I4210128921","display_name":"The First Affiliated Hospital, Sun Yat-sen University","ror":"https://ror.org/037p24858","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210128921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Anjia Han","raw_affiliation_strings":["The First Affiliated Hospital of Sun Yat-sen University,Department of Pathology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The First Affiliated Hospital of Sun Yat-sen University,Department of Pathology,Guangzhou,China","institution_ids":["https://openalex.org/I157773358","https://openalex.org/I4210128921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124227604","display_name":"Yonghong He","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghong He","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School, Tsinghua University,Shenzhen,China","institution_ids":["https://openalex.org/I4210114105","https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1576","last_page":"1581"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.887499988079071,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.887499988079071,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.016499999910593033,"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":0.01269999984651804,"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/feature-learning","display_name":"Feature learning","score":0.671500027179718},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6536999940872192},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.6398000121116638},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6237999796867371},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5507000088691711},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.46560001373291016},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4505999982357025},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4212000072002411},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.3930000066757202}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7976999878883362},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7081999778747559},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.671500027179718},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6536999940872192},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.6398000121116638},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6237999796867371},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5507000088691711},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48669999837875366},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.46560001373291016},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4505999982357025},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4212000072002411},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3930000066757202},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37619999051094055},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.36570000648498535},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3619999885559082},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356305","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356305","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5104051232337952}],"awards":[{"id":"https://openalex.org/G5261752650","display_name":null,"funder_award_id":"82430062","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2148309496","https://openalex.org/W2329674354","https://openalex.org/W2795989238","https://openalex.org/W3135547872","https://openalex.org/W3176719058","https://openalex.org/W3203838058","https://openalex.org/W4205900565","https://openalex.org/W4295917821","https://openalex.org/W4316038054","https://openalex.org/W4317473799","https://openalex.org/W4385948838","https://openalex.org/W4386071624","https://openalex.org/W4402125280","https://openalex.org/W4402716046","https://openalex.org/W4402716098","https://openalex.org/W4402733589","https://openalex.org/W4403365569","https://openalex.org/W4403719672"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,9,79,85,119,144],"rapid":[2],"advancement":[3],"of":[4,12,82,161],"pathology":[5],"foundation":[6],"models":[7],"(FMs),":[8],"representation":[10,42,71,103],"learning":[11,43,50,104],"whole":[13],"slide":[14,41,70,102,154],"images":[15],"(WSIs)":[16],"attracts":[17],"increasing":[18],"attention.":[19],"Existing":[20],"studies":[21,67],"develop":[22],"high-quality":[23],"patch":[24],"feature":[25],"extractors":[26],"and":[27,148,177],"employ":[28],"carefully":[29],"designed":[30],"aggregation":[31,140],"schemes":[32],"to":[33,54,114,129,151,157,181],"derive":[34],"slide-level":[35],"representations.":[36],"However,":[37,73],"mainstream":[38],"weakly":[39,183],"supervised":[40,184],"methods,":[44],"primarily":[45],"based":[46],"on":[47,78,166],"multiple":[48],"instance":[49],"(MIL),":[51],"are":[52],"tailored":[53],"specific":[55],"downstream":[56,162],"tasks,":[57],"which":[58],"limits":[59],"their":[60],"generalizability.":[61],"To":[62],"address":[63],"this":[64,94],"issue,":[65],"some":[66,182],"explore":[68],"unsupervised":[69,101,153,175],"learning.":[72],"these":[74,149],"approaches":[75],"focus":[76],"solely":[77],"visual":[80],"modality":[81],"patches,":[83],"neglecting":[84],"rich":[86],"semantic":[87],"information":[88],"embedded":[89],"in":[90,123],"textual":[91],"data.":[92],"In":[93],"work,":[95],"we":[96,107,135],"propose":[97,136],"ProAlign,":[98],"a":[99,109,124,137,158],"cross-modal":[100],"framework.":[105],"Specifically,":[106],"leverage":[108],"large":[110],"language":[111],"model":[112],"(LLM)":[113],"generate":[115],"descriptive":[116],"text":[117],"for":[118],"prototype":[120,132],"types":[121],"present":[122],"WSI,":[125],"introducing":[126],"patch-text":[127],"contrast":[128],"construct":[130],"initial":[131],"embeddings.":[133],"Furthermore,":[134],"parameter-free":[138],"attention":[139],"strategy":[141],"that":[142,171],"utilizes":[143],"similarity":[145],"between":[146],"patches":[147],"prototypes":[150],"form":[152],"embeddings":[155],"applicable":[156],"wide":[159],"range":[160],"tasks.":[163],"Extensive":[164],"experiments":[165],"four":[167],"public":[168],"datasets":[169],"show":[170],"ProAlign":[172],"outperforms":[173],"existing":[174],"frameworks":[176],"achieves":[178],"performance":[179],"comparable":[180],"models.":[185]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2026-01-30T00:00:00"}
