{"id":"https://openalex.org/W7126041991","doi":"https://doi.org/10.1109/bibm66473.2025.11356660","title":"A Multi-Branch Contrastive Learning Feature Fusion Network for Breast Ultrasound Images","display_name":"A Multi-Branch Contrastive Learning Feature Fusion Network for Breast Ultrasound Images","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126041991","doi":"https://doi.org/10.1109/bibm66473.2025.11356660"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356660","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356660","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/A5112167451","display_name":"Songming Zheng","orcid":"https://orcid.org/0009-0007-9484-7390"},"institutions":[{"id":"https://openalex.org/I116265982","display_name":"Qinghai University","ror":"https://ror.org/05h33bt13","country_code":"CN","type":"education","lineage":["https://openalex.org/I116265982"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Songming Zheng","raw_affiliation_strings":["School of Computer Technology and Application, Qinghai University,Xining,China,810000"],"affiliations":[{"raw_affiliation_string":"School of Computer Technology and Application, Qinghai University,Xining,China,810000","institution_ids":["https://openalex.org/I116265982"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111286332","display_name":"Wangxing Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I78978612","display_name":"Yangzhou University","ror":"https://ror.org/03tqb8s11","country_code":"CN","type":"education","lineage":["https://openalex.org/I78978612"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wangxing Huang","raw_affiliation_strings":["Clinical Medical College, Yangzhou University,Yangzhou,China"],"affiliations":[{"raw_affiliation_string":"Clinical Medical College, Yangzhou University,Yangzhou,China","institution_ids":["https://openalex.org/I78978612"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124188249","display_name":"Dong Li","orcid":null},"institutions":[{"id":"https://openalex.org/I116265982","display_name":"Qinghai University","ror":"https://ror.org/05h33bt13","country_code":"CN","type":"education","lineage":["https://openalex.org/I116265982"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Li","raw_affiliation_strings":["School of Computer Technology and Application, Qinghai University,Xining,China,810000"],"affiliations":[{"raw_affiliation_string":"School of Computer Technology and Application, Qinghai University,Xining,China,810000","institution_ids":["https://openalex.org/I116265982"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124186547","display_name":"Xikun Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I116265982","display_name":"Qinghai University","ror":"https://ror.org/05h33bt13","country_code":"CN","type":"education","lineage":["https://openalex.org/I116265982"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xikun Meng","raw_affiliation_strings":["School of Computer Technology and Application, Qinghai University,Xining,China,810000"],"affiliations":[{"raw_affiliation_string":"School of Computer Technology and Application, Qinghai University,Xining,China,810000","institution_ids":["https://openalex.org/I116265982"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124256567","display_name":"Yuqing Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I116265982","display_name":"Qinghai University","ror":"https://ror.org/05h33bt13","country_code":"CN","type":"education","lineage":["https://openalex.org/I116265982"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqing Dong","raw_affiliation_strings":["School of Computer Technology and Application, Qinghai University,Xining,China,810000"],"affiliations":[{"raw_affiliation_string":"School of Computer Technology and Application, Qinghai University,Xining,China,810000","institution_ids":["https://openalex.org/I116265982"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001838653","display_name":"Tong Zheng","orcid":"https://orcid.org/0000-0001-6894-3521"},"institutions":[{"id":"https://openalex.org/I116265982","display_name":"Qinghai University","ror":"https://ror.org/05h33bt13","country_code":"CN","type":"education","lineage":["https://openalex.org/I116265982"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tong Zheng","raw_affiliation_strings":["School of Computer Technology and Application, Qinghai University,Xining,China,810000"],"affiliations":[{"raw_affiliation_string":"School of Computer Technology and Application, Qinghai University,Xining,China,810000","institution_ids":["https://openalex.org/I116265982"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5112167451"],"corresponding_institution_ids":["https://openalex.org/I116265982"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71080252,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5107","last_page":"5114"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.4447999894618988,"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"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.4447999894618988,"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"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.32190001010894775,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.059700001031160355,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6593999862670898},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5727999806404114},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.536899983882904},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4740999937057495},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4510999917984009},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4341999888420105},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.3995000123977661},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.396699994802475}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7777000069618225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6931999921798706},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6593999862670898},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5727999806404114},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.536899983882904},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4510999917984009},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4341999888420105},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.396699994802475},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.3765000104904175},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.3330000042915344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31779998540878296},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C2777432617","wikidata":"https://www.wikidata.org/wiki/Q22905905","display_name":"Breast imaging","level":5,"score":0.27570000290870667},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2540999948978424},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356660","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356660","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":[{"score":0.6687684655189514,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G6121566539","display_name":null,"funder_award_id":"62366043","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":27,"referenced_works":["https://openalex.org/W1539811621","https://openalex.org/W2097475056","https://openalex.org/W2767128594","https://openalex.org/W2791015340","https://openalex.org/W2912993657","https://openalex.org/W2947267495","https://openalex.org/W2985260969","https://openalex.org/W2991372685","https://openalex.org/W3002592716","https://openalex.org/W3006104185","https://openalex.org/W3023189324","https://openalex.org/W3035524453","https://openalex.org/W3045691625","https://openalex.org/W3130362871","https://openalex.org/W3140854437","https://openalex.org/W3162357183","https://openalex.org/W3168837030","https://openalex.org/W3175593095","https://openalex.org/W4207017358","https://openalex.org/W4213019189","https://openalex.org/W4214920103","https://openalex.org/W4225009244","https://openalex.org/W4286470556","https://openalex.org/W4308119975","https://openalex.org/W4390410643","https://openalex.org/W4402937008","https://openalex.org/W4405465625"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"classification":[1,31],"of":[2,11,161,191],"breast":[3,12,174],"ultrasound":[4,175],"images":[5,127],"is":[6,68],"crucial":[7],"for":[8],"early":[9],"diagnosis":[10],"cancer.":[13],"Fusing":[14],"quantitative":[15],"radiomics":[16,86],"features":[17,95,111,124],"with":[18],"visual":[19],"patterns":[20],"from":[21,112,125],"deep":[22],"learning":[23,138],"offers":[24],"complementary":[25],"perspectives":[26],"that":[27,84,140,178],"can":[28,152],"significantly":[29,153],"enhance":[30,154],"accuracy":[32],"and":[33,45,92,105,123,143,147,158,171,185,194],"robustness.":[34],"However,":[35],"existing":[36,186],"fusion":[37,187],"methods":[38,188],"are":[39],"often":[40],"limited":[41],"by":[42,109],"semantic":[43,156],"gaps":[44],"distribution":[46],"discrepancies":[47],"among":[48],"heterogeneous":[49],"features.":[50,164],"To":[51],"address":[52],"this,":[53],"this":[54],"study":[55],"proposes":[56],"a":[57,81,136,172],"Multi-Branch":[58],"Contrastive":[59,70],"Learning":[60,71],"Feature":[61],"Fusion":[62,74],"Network":[63],"(MBCF-Net),":[64],"whose":[65],"core":[66],"component":[67],"the":[69,101,113,132,155,162],"Guided":[72],"Cross-branch":[73],"(CLGCF)":[75],"module.":[76],"The":[77],"CLGCF":[78,102,133],"module":[79,134],"employs":[80,135],"three-branch":[82],"architecture":[83],"integrates":[85],"features,":[87,91],"Vision":[88],"Transformer-captured":[89],"global":[90],"CNN-extracted":[93],"local":[94],"through":[96,117],"crossmodal":[97],"attention":[98],"mechanisms.":[99],"Specifically,":[100],"constructs":[103,141],"positive":[104,121,142],"negative":[106,129,144],"sample":[107,145],"pairs":[108,146],"treating":[110],"same":[114],"image":[115],"processed":[116],"different":[118,126],"branches":[119],"as":[120,128],"samples":[122],"samples.":[130],"Because":[131],"contrastive":[137],"mechanism":[139],"utilizes":[148],"InfoNCE":[149],"loss,":[150],"it":[151],"consistency":[157],"discriminative":[159],"power":[160],"fused":[163],"Comprehensive":[165],"evaluations":[166],"on":[167],"both":[168],"public":[169],"datasets":[170],"private":[173],"dataset":[176],"demonstrate":[177],"MBCF-Net":[179],"substantially":[180],"outperforms":[181],"traditional":[182],"unimodal":[183],"approaches":[184],"in":[189],"terms":[190],"accuracy,":[192],"robustness,":[193],"generalization":[195],"ability.":[196]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-30T00:00:00"}
