{"id":"https://openalex.org/W7126049523","doi":"https://doi.org/10.1109/bibm66473.2025.11356519","title":"Cross-Stain Contrastive Learning for Paired Immunohistochemistry and Histopathology Slide Representation Learning","display_name":"Cross-Stain Contrastive Learning for Paired Immunohistochemistry and Histopathology Slide Representation Learning","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126049523","doi":"https://doi.org/10.1109/bibm66473.2025.11356519"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356519","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356519","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":"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/A5101789838","display_name":"Yizhi Zhang","orcid":"https://orcid.org/0009-0000-5586-5886"},"institutions":[{"id":"https://openalex.org/I75689368","display_name":"Communication University of China","ror":"https://ror.org/04facbs33","country_code":"CN","type":"education","lineage":["https://openalex.org/I75689368"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yizhi Zhang","raw_affiliation_strings":["Communication University of China,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Communication University of China,Beijing,China","institution_ids":["https://openalex.org/I75689368"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059040724","display_name":"Lei Fan","orcid":"https://orcid.org/0000-0001-9472-7152"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I4210115840","display_name":"Migration Institute of Australia","ror":"https://ror.org/01hg8wj19","country_code":"AU","type":"other","lineage":["https://openalex.org/I4210115840"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lei Fan","raw_affiliation_strings":["UNSW Sydney,Sydney,Australia"],"affiliations":[{"raw_affiliation_string":"UNSW Sydney,Sydney,Australia","institution_ids":["https://openalex.org/I4210115840","https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114483022","display_name":"Zhulin Tao","orcid":null},"institutions":[{"id":"https://openalex.org/I75689368","display_name":"Communication University of China","ror":"https://ror.org/04facbs33","country_code":"CN","type":"education","lineage":["https://openalex.org/I75689368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhulin Tao","raw_affiliation_strings":["Communication University of China,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Communication University of China,Beijing,China","institution_ids":["https://openalex.org/I75689368"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031208819","display_name":"Donglin Di","orcid":"https://orcid.org/0000-0002-2270-3378"},"institutions":[{"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":"Donglin Di","raw_affiliation_strings":["Tsinghua University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124286828","display_name":"Yang Song","orcid":null},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I4210115840","display_name":"Migration Institute of Australia","ror":"https://ror.org/01hg8wj19","country_code":"AU","type":"other","lineage":["https://openalex.org/I4210115840"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Yang Song","raw_affiliation_strings":["UNSW Sydney,Sydney,Australia"],"affiliations":[{"raw_affiliation_string":"UNSW Sydney,Sydney,Australia","institution_ids":["https://openalex.org/I4210115840","https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124160857","display_name":"Sidong Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sidong Liu","raw_affiliation_strings":["Macquarie University,Sydney,Australia"],"affiliations":[{"raw_affiliation_string":"Macquarie University,Sydney,Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101567282","display_name":"Cong Cong","orcid":"https://orcid.org/0000-0002-8192-6731"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Cong Cong","raw_affiliation_strings":["Macquarie University,Sydney,Australia"],"affiliations":[{"raw_affiliation_string":"Macquarie University,Sydney,Australia","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101789838"],"corresponding_institution_ids":["https://openalex.org/I75689368"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87361246,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1458","last_page":"1463"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9781000018119812,"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":0.9781000018119812,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.010300000198185444,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.004000000189989805,"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/feature-learning","display_name":"Feature learning","score":0.5497000217437744},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4641999900341034},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44760000705718994},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.3928000032901764},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3303999900817871},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.31380000710487366}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7222999930381775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6274999976158142},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5497000217437744},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4641999900341034},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41429999470710754},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3928000032901764},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3880000114440918},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3303999900817871},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C3018790387","wikidata":"https://www.wikidata.org/wiki/Q869010","display_name":"Hybrid learning","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.2750999927520752}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356519","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356519","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1986241711","https://openalex.org/W2068523052","https://openalex.org/W2096283457","https://openalex.org/W3034275286","https://openalex.org/W3043075211","https://openalex.org/W3175126073","https://openalex.org/W3176719058","https://openalex.org/W3203263549","https://openalex.org/W3207232829","https://openalex.org/W3209526810","https://openalex.org/W4225010001","https://openalex.org/W4295917821","https://openalex.org/W4295935120","https://openalex.org/W4312468136","https://openalex.org/W4312504504","https://openalex.org/W4379878309","https://openalex.org/W4385948838","https://openalex.org/W4386784567","https://openalex.org/W4387259074","https://openalex.org/W4392947521","https://openalex.org/W4392947532","https://openalex.org/W4394998094","https://openalex.org/W4398201291","https://openalex.org/W4400188830","https://openalex.org/W4402733572","https://openalex.org/W4403726636","https://openalex.org/W4404879644","https://openalex.org/W4413146145","https://openalex.org/W4414360208","https://openalex.org/W4414360603","https://openalex.org/W4414368112"],"related_works":[],"abstract_inverted_index":{"Universal,":[0],"transferable":[1,164],"whole-slide":[2],"image":[3],"(WSI)":[4],"representations":[5],"are":[6,172],"central":[7],"to":[8,68,98,126,137],"computational":[9],"pathology.":[10],"Incorporating":[11],"multiple":[12],"markers":[13],"(e.g.,":[14],"immunohistochemistry,":[15],"IHC)":[16],"alongside":[17],"H&E":[18,103,165],"enriches":[19],"H&E-based":[20],"features":[21,48,104,130],"with":[22,94,105,114],"diverse,":[23],"biologically":[24],"meaningful":[25],"information.":[26],"However,":[27],"progress":[28],"is":[29],"limited":[30],"by":[31,161],"the":[32,100],"scarcity":[33],"of":[34,102],"well-aligned":[35],"multi-stain":[36],"datasets.":[37],"Inter-stain":[38],"Misalignment":[39],"shifts":[40],"corresponding":[41,106],"tissue":[42],"across":[43,143],"slides,":[44],"hindering":[45],"consistent":[46,159],"patch-level":[47],"and":[49,73,110,131,155,170],"degrading":[50],"slide-level":[51,59,111,141,166],"embeddings.":[52],"To":[53],"address":[54],"this,":[55],"we":[56,80],"curated":[57],"a":[58,86,90,121,132],"aligned,":[60],"five-stain":[61],"dataset":[62],"(H&E,":[63],"HER2,":[64],"KI67,":[65],"ER,":[66],"PGR)":[67],"enable":[69],"paired":[70],"H&E-IHC":[71],"learning":[72,113],"robust":[74],"cross-stain":[75,122],"representation.":[76],"Leveraging":[77],"this":[78],"dataset,":[79],"propose":[81],"Cross-Stain":[82],"Contrastive":[83],"Learning":[84,117],"(CSCL),":[85],"two-stage":[87],"pretraining":[88],"framework:":[89],"lightweight":[91],"adapter":[92],"trained":[93],"patch-wise":[95],"contrastive":[96],"alignment":[97,135],"improve":[99],"compatibility":[101],"IHC-derived":[107],"contextual":[108],"cues;":[109],"representation":[112],"Multiple":[115],"Instance":[116],"(MIL),":[118],"which":[119],"uses":[120],"attention":[123],"fusion":[124],"module":[125,136],"integrate":[127],"stain-specific":[128],"patch":[129],"crossstain":[133],"global":[134],"enforce":[138],"consistency":[139],"among":[140],"embeddings":[142],"different":[144],"stains.":[145],"Experiments":[146],"on":[147],"cancer":[148],"subtype":[149],"classification,":[150,154],"IHC":[151],"biomarker":[152],"status":[153],"survival":[156],"prediction,":[157],"show":[158],"gains":[160],"yielding":[162],"high-quality,":[163],"representations.":[167],"The":[168],"code":[169],"data":[171],"available":[173],"at:":[174],"https://github.com/lily-zyz/CSCL.":[175]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}
