{"id":"https://openalex.org/W4416144463","doi":"https://doi.org/10.1145/3721201.3725439","title":"Pathological Complete Response Classification on HER2-Positive Breast Cancer Patients with Double-View Graph Contrastive Learning","display_name":"Pathological Complete Response Classification on HER2-Positive Breast Cancer Patients with Double-View Graph Contrastive Learning","publication_year":2025,"publication_date":"2025-06-24","ids":{"openalex":"https://openalex.org/W4416144463","doi":"https://doi.org/10.1145/3721201.3725439"},"language":null,"primary_location":{"id":"doi:10.1145/3721201.3725439","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3721201.3725439","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3725439","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3725439","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101109229","display_name":"Dechuang Jiao","orcid":"https://orcid.org/0009-0003-7441-9103"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]},{"id":"https://openalex.org/I4210143608","display_name":"Henan Cancer Hospital","ror":"https://ror.org/043ek5g31","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210143608"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dechuang Jiao","raw_affiliation_strings":["The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China"],"raw_orcid":"https://orcid.org/0009-0003-7441-9103","affiliations":[{"raw_affiliation_string":"The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, China","institution_ids":["https://openalex.org/I4210143608","https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yanqiu Shao","orcid":"https://orcid.org/0009-0006-0198-754X"},"institutions":[{"id":"https://openalex.org/I4210091812","display_name":"Bristol-Myers Squibb (United States)","ror":"https://ror.org/00gtmwv55","country_code":"US","type":"company","lineage":["https://openalex.org/I4210091812"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanqiu Shao","raw_affiliation_strings":["Bristol Myers Squibb, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0009-0006-0198-754X","affiliations":[{"raw_affiliation_string":"Bristol Myers Squibb, Princeton, NJ, USA","institution_ids":["https://openalex.org/I4210091812"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039175523","display_name":"Xiaohan Guo","orcid":"https://orcid.org/0000-0002-0423-9882"},"institutions":[{"id":"https://openalex.org/I180857899","display_name":"Pfizer (United States)","ror":"https://ror.org/01xdqrp08","country_code":"US","type":"company","lineage":["https://openalex.org/I180857899"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaohan Guo","raw_affiliation_strings":["Pfizer, Manhattan, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-0423-9882","affiliations":[{"raw_affiliation_string":"Pfizer, Manhattan, NY, USA","institution_ids":["https://openalex.org/I180857899"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102634878","display_name":"Zihan Lin","orcid":"https://orcid.org/0000-0002-9394-5593"},"institutions":[{"id":"https://openalex.org/I4210091812","display_name":"Bristol-Myers Squibb (United States)","ror":"https://ror.org/00gtmwv55","country_code":"US","type":"company","lineage":["https://openalex.org/I4210091812"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zihan Lin","raw_affiliation_strings":["Bristol Myers Squibb, Princeton, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0002-9394-5593","affiliations":[{"raw_affiliation_string":"Bristol Myers Squibb, Princeton, NJ, USA","institution_ids":["https://openalex.org/I4210091812"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076791243","display_name":"Junchao Fei","orcid":"https://orcid.org/0009-0002-3273-0094"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junchao Fei","raw_affiliation_strings":["Temple University, Philadelphia, PA, USA"],"raw_orcid":"https://orcid.org/0009-0002-3273-0094","affiliations":[{"raw_affiliation_string":"Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chenguang Yang","orcid":"https://orcid.org/0009-0003-3048-1829"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenguang Yang","raw_affiliation_strings":["University of California, Riverside, Riverside, CA, USA"],"raw_orcid":"https://orcid.org/0009-0003-3048-1829","affiliations":[{"raw_affiliation_string":"University of California, Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024040830","display_name":"Yuzhou Chen","orcid":"https://orcid.org/0000-0001-7945-7940"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuzhou Chen","raw_affiliation_strings":["University of California, Riverside, Riverside, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-7945-7940","affiliations":[{"raw_affiliation_string":"University of California, Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011747432","display_name":"Huanmei Wu","orcid":"https://orcid.org/0000-0003-0346-6044"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Huanmei Wu","raw_affiliation_strings":["Temple University, Philadelphia, PA, USA"],"raw_orcid":"https://orcid.org/0000-0003-0346-6044","affiliations":[{"raw_affiliation_string":"Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16265505,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"365","last_page":"370"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.6444000005722046,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.6444000005722046,"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/T10862","display_name":"AI in cancer detection","score":0.1543000042438507,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.024700000882148743,"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/breast-cancer","display_name":"Breast cancer","score":0.604200005531311},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5616000294685364},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4878000020980835},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4025000035762787},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4018999934196472},{"id":"https://openalex.org/keywords/pathological","display_name":"Pathological","score":0.34040001034736633},{"id":"https://openalex.org/keywords/transformative-learning","display_name":"Transformative learning","score":0.33309999108314514}],"concepts":[{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.604200005531311},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5665000081062317},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5616000294685364},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4878000020980835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4837000072002411},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4058000147342682},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4025000035762787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4018999934196472},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3882000148296356},{"id":"https://openalex.org/C207886595","wikidata":"https://www.wikidata.org/wiki/Q1456138","display_name":"Pathological","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C70587473","wikidata":"https://www.wikidata.org/wiki/Q7834111","display_name":"Transformative learning","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C53811970","wikidata":"https://www.wikidata.org/wiki/Q5062194","display_name":"Centrality","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C28225019","wikidata":"https://www.wikidata.org/wiki/Q4915005","display_name":"Biological network","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.2727999985218048},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.27000001072883606},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3721201.3725439","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3721201.3725439","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3725439","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3721201.3725439","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3721201.3725439","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3721201.3725439","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416144463.pdf","grobid_xml":"https://content.openalex.org/works/W4416144463.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W2075894019","https://openalex.org/W2122925692","https://openalex.org/W2141393790","https://openalex.org/W2167108935","https://openalex.org/W2226378560","https://openalex.org/W2806209829","https://openalex.org/W2981669117","https://openalex.org/W3010336026","https://openalex.org/W3036446966","https://openalex.org/W3095746859","https://openalex.org/W3096932862","https://openalex.org/W3215452784","https://openalex.org/W4393147402"],"related_works":[],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"tremendous":[3],"success":[4],"has":[5],"been":[6],"witnessed":[7],"in":[8,69,188],"Graph":[9,122],"Neural":[10],"Networks":[11],"(GNNs),":[12],"widely":[13],"used":[14],"to":[15,161],"various":[16],"applications":[17],"such":[18],"as":[19],"social":[20],"network":[21],"analysis,":[22],"recommendation":[23],"systems,":[24,26],"energy":[25],"and":[27,59,72,104,201],"biomedicine.":[28],"HER2-positive":[29,77,189],"breast":[30,56,78,190],"cancer":[31,57,191],"is":[32,51,110],"a":[33,126,154,172,185],"biologically":[34],"distinct":[35],"subtype":[36],"characterized":[37],"by":[38],"the":[39,42,54,139,176,195],"overexpression":[40],"of":[41,53,197],"human":[43],"epidermal":[44],"growth":[45],"factor":[46],"receptor":[47],"2":[48],"(HER2)":[49],"which":[50],"one":[52],"primary":[55],"cases,":[58],"there":[60],"are":[61],"many":[62],"GNN-based":[63],"models":[64],"have":[65],"offered":[66],"transformative":[67],"capabilities":[68],"diagnosis,":[70],"prognosis,":[71],"treatment":[73],"response":[74,142],"prediction":[75],"for":[76,138],"cancer.":[79],"However,":[80],"most":[81],"existing":[82],"methods":[83],"often":[84],"rely":[85],"on":[86,100,113,171],"fixed":[87],"graph":[88,128,152,198],"structures":[89],"that":[90],"may":[91],"not":[92],"fully":[93],"represent":[94],"complex":[95],"interactions":[96],"among":[97],"patients":[98],"based":[99],"their":[101,105],"baseline":[102],"characteristics,":[103],"resulting":[106],"node/graph":[107],"embedding":[108],"quality":[109],"heavily":[111],"dependent":[112],"labeled":[114],"data.":[115],"To":[116],"address":[117],"this,":[118],"we":[119,146],"propose":[120],"Double-View":[121],"Contrastive":[123],"Learning":[124],"(DOVE-GCL),":[125],"novel":[127],"contrastive":[129],"learning":[130,200],"framework":[131],"leveraging":[132],"two":[133],"different":[134],"patient-patient":[135,150],"similarity":[136,151],"graphs":[137],"pathological":[140],"complete":[141],"classification":[143],"task.":[144],"Moreover,":[145],"develop":[147],"an":[148],"innovative":[149],"using":[153],"multi-agent":[155],"Large":[156],"Language":[157],"Model":[158],"(LLM)-based":[159],"system":[160],"capture":[162],"domain":[163],"specific":[164],"knowledge":[165],"about":[166],"patients.":[167],"We":[168],"evaluate":[169],"DOVE-GCL":[170],"real-world":[173],"dataset,":[174],"with":[175],"model":[177],"showing":[178],"competitive":[179],"performance.":[180],"Our":[181],"work,":[182],"therefore,":[183],"marks":[184],"significant":[186],"step":[187],"patient":[192],"analysis":[193],"within":[194],"realms":[196],"deep":[199],"LLMs.":[202]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-12T00:00:00"}