{"id":"https://openalex.org/W7160294461","doi":"https://doi.org/10.48550/arxiv.2605.02126","title":"Ultrasound Vision-Language Alignment via Contrastive Learning","display_name":"Ultrasound Vision-Language Alignment via Contrastive Learning","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160294461","doi":"https://doi.org/10.48550/arxiv.2605.02126"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02126","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02126","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.02126","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126242766","display_name":"Zhuoyang Lyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lyu, Zhuoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135399688","display_name":"Yiyang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079050779","display_name":"Tongxin Wang","orcid":"https://orcid.org/0000-0001-5826-1842"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tongxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135390117","display_name":"Ruirui Lan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lan, Ruirui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.5009999871253967,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.5009999871253967,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.11999999731779099,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.1006999984383583,"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/encoder","display_name":"Encoder","score":0.5612000226974487},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5598999857902527},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4952999949455261},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.40450000762939453},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.38920000195503235},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3849000036716461},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3662000000476837},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.3598000109195709}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7685999870300293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5702000260353088},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5612000226974487},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5598999857902527},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5171999931335449},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4952999949455261},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4366999864578247},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.38920000195503235},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3849000036716461},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.3598000109195709},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.3555000126361847},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C2776035091","wikidata":"https://www.wikidata.org/wiki/Q7928819","display_name":"Viewpoints","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32190001010894775},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3100999891757965},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02126","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02126","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.02126","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02126","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Ultrasound":[0],"foundation":[1],"models":[2,114],"have":[3],"achieved":[4],"strong":[5],"performance":[6],"on":[7,151,156,159],"structured":[8],"prediction":[9],"tasks":[10,22],"but":[11,225],"remain":[12],"exclusively":[13],"vision-based,":[14],"limiting":[15],"zero-shot":[16,149],"and":[17,65,76,84,100,109,173,186,237],"few-shot":[18,174],"transfer":[19,166,228],"to":[20,168],"novel":[21],"where":[23],"task-specific":[24],"annotation":[25],"is":[26,203,219],"scarce.":[27],"We":[28,51,89,189],"address":[29],"this":[30],"gap":[31],"with":[32,43,67,80,121,143],"EchoCare-CLIP,":[33],"a":[34,47,53,81,126,181,205],"CLIP-style":[35],"dual-encoder":[36],"contrastive":[37],"framework":[38],"that":[39,192,215],"aligns":[40],"ultrasound":[41,216],"images":[42],"clinical":[44,227],"text":[45,95],"in":[46],"shared":[48],"embedding":[49],"space.":[50],"curate":[52],"multi-organ":[54],"corpus":[55],"of":[56,70,130,232],"over":[57,68,119],"16K":[58],"image-text":[59],"pairs":[60],"spanning":[61,93],"breast,":[62],"liver,":[63],"lung,":[64],"thyroid,":[66],"78%":[69],"captions":[71,194],"derived":[72],"from":[73,221],"expert-annotated":[74],"reports,":[75],"complement":[77],"the":[78,122,147],"remainder":[79],"three-tier":[82],"template-based":[83,193],"LLM-based":[85],"caption":[86,102,208,238],"generation":[87],"pipeline.":[88],"evaluate":[90],"model":[91,176],"configurations":[92],"two":[94,101],"encoder":[96,235],"families":[97],"(CLIP,":[98],"BioClinicalBERT)":[99],"strategies":[103],"(template-based,":[104],"LLM-generated)":[105],"against":[106],"OpenAI":[107],"CLIP":[108],"BiomedCLIP":[110],"baselines.":[111],"Our":[112],"trained":[113],"consistently":[115],"improve":[116],"cross-modal":[117],"alignment":[118,128,134,218],"baselines,":[120],"best":[123],"configuration":[124],"achieving":[125],"paired":[127],"score":[129],"0.682.":[131],"However,":[132],"stronger":[133],"does":[135],"not":[136,204],"guarantee":[137],"better":[138],"downstream":[139],"performance:":[140],"CLIP-based":[141],"variants":[142],"partial":[144],"fine-tuning":[145,164],"achieve":[146],"strongest":[148],"classification":[150],"external":[152],"held-out":[153],"datasets":[154],"(0.709":[155],"BUSI;":[157],"0.626":[158],"AULI),":[160],"while":[161],"full":[162],"end-to-end":[163],"degrades":[165],"due":[167],"overfitting.":[169],"On":[170],"linear":[171],"probing":[172],"adaptation,":[175,234],"rankings":[177],"are":[178],"dataset-dependent,":[179],"reflecting":[180],"trade-off":[182],"between":[183],"domain":[184,233],"adaptation":[185],"representational":[187],"generalizability.":[188],"further":[190],"show":[191],"match":[195],"or":[196],"outperform":[197],"LLM-generated":[198],"captions,":[199],"suggesting":[200],"lexical":[201],"diversity":[202],"proxy":[206],"for":[207],"quality.":[209,240],"Taken":[210],"together,":[211],"our":[212],"results":[213],"demonstrate":[214],"vision-language":[217],"achievable":[220],"public":[222],"data":[223],"alone,":[224],"robust":[226],"requires":[229],"careful":[230],"balancing":[231],"capacity,":[236],"supervision":[239]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}