{"id":"https://openalex.org/W7161770468","doi":"https://doi.org/10.1109/isbi61048.2026.11515746","title":"Towards Universal Ultrasound Analysis: Parameter-Efficient Foundation Model With Task-Aware Routing for Heterogeneous Multi-Task Learning","display_name":"Towards Universal Ultrasound Analysis: Parameter-Efficient Foundation Model With Task-Aware Routing for Heterogeneous Multi-Task Learning","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7161770468","doi":"https://doi.org/10.1109/isbi61048.2026.11515746"},"language":null,"primary_location":{"id":"doi:10.1109/isbi61048.2026.11515746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515746","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","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/A5006557503","display_name":"Y Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yixuan Zhang","raw_affiliation_strings":["University of Nottingham Ningbo China,Ningbo,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Nottingham Ningbo China,Ningbo,China","institution_ids":["https://openalex.org/I13591777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102087196","display_name":"Qing Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Xu","raw_affiliation_strings":["University of Nottingham Ningbo China,Ningbo,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Nottingham Ningbo China,Ningbo,China","institution_ids":["https://openalex.org/I13591777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055037189","display_name":"Yuxia Li","orcid":"https://orcid.org/0009-0002-4723-4915"},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Li","raw_affiliation_strings":["University of Nottingham Ningbo China,Ningbo,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Nottingham Ningbo China,Ningbo,China","institution_ids":["https://openalex.org/I13591777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136537780","display_name":"Xiangjian He","orcid":null},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangjian He","raw_affiliation_strings":["University of Nottingham Ningbo China,Ningbo,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Nottingham Ningbo China,Ningbo,China","institution_ids":["https://openalex.org/I13591777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136586139","display_name":"Zhen Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zhen Chen","raw_affiliation_strings":["The Hong Kong Polytechnic University,Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University,Hong Kong","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5006557503"],"corresponding_institution_ids":["https://openalex.org/I13591777"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.93722576,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5447999835014343,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.5447999835014343,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.0649000033736229,"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/T10727","display_name":"Ultrasound Imaging and Elastography","score":0.056699998676776886,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.49230000376701355},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.4154999852180481},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2992999851703644},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.27410000562667847},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.26409998536109924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5486999750137329},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.49230000376701355},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.4154999852180481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33489999175071716},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2822999954223633},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.22920000553131104}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi61048.2026.11515746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi61048.2026.11515746","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2913559493","https://openalex.org/W3085046840","https://openalex.org/W3211490618","https://openalex.org/W4210300257","https://openalex.org/W4312685179","https://openalex.org/W4381430202","https://openalex.org/W4396599322","https://openalex.org/W4406542711","https://openalex.org/W4410295046","https://openalex.org/W7155365227"],"related_works":[],"abstract_inverted_index":{"Developing":[0],"a":[1,54,80,116,122,136,176],"universal":[2,207],"foundation":[3,108],"model":[4],"for":[5,68,132,145,206],"ultrasound":[6,208],"imaging":[7],"is":[8],"inherently":[9],"challenging":[10],"due":[11],"to":[12,91,159],"severe":[13],"speckle":[14],"noise,":[15],"diverse":[16,111],"acquisition":[17],"protocols,":[18],"and":[19,42,110,135,153,194,202],"the":[20,64,103,168,180],"semantic":[21],"gap":[22],"between":[23,106],"heterogeneous":[24],"clinical":[25],"tasks.":[26,147],"Traditional":[27],"multi-task":[28],"learning":[29],"often":[30],"suffers":[31],"from":[32],"negative":[33],"transfer":[34],"when":[35],"simultaneously":[36],"optimizing":[37],"dense":[38,133],"(i.e.,":[39,44],"segmentation,":[40],"detection)":[41],"global":[43,143],"classification,":[45],"regression)":[46],"predictions.":[47],"In":[48],"this":[49],"paper,":[50],"we":[51,78,114],"propose":[52],"UltraTAR,":[53],"parameter-efficient":[55],"Task-Aware":[56],"Routing":[57],"framework":[58],"that":[59,127,141],"won":[60],"1st":[61],"Place":[62],"in":[63,191,199],"Foundation":[65,83],"Model":[66,84],"Challenge":[67],"Ultrasound":[69],"Image":[70],"Analysis":[71],"(FMC-UIA)":[72],"2026.":[73],"Instead":[74],"of":[75],"full":[76],"fine-tuning,":[77],"leverage":[79],"frozen":[81],"Vision":[82],"(VFM)":[85],"adapted":[86],"via":[87],"Low-Rank":[88],"Adaptation":[89],"(LoRA)":[90],"preserve":[92],"robust":[93],"pre-trained":[94],"representations":[95,131],"while":[96],"injecting":[97],"lightweight":[98],"task-specific":[99],"knowledge.":[100],"To":[101],"overcome":[102],"architectural":[104],"disconnect":[105],"single-scale":[107],"features":[109],"task":[112],"requirements,":[113],"design":[115],"task-aware":[117],"dual-branch":[118],"routing":[119],"strategy,":[120],"including":[121],"novel":[123],"Feature":[124],"Pyramid":[125],"Adapter":[126],"constructs":[128],"hierarchical":[129],"FPN-compatible":[130],"tasks,":[134],"CLS-guided":[137],"feature":[138],"fusion":[139],"module":[140],"enriches":[142],"context":[144],"diagnostic":[146],"Furthermore,":[148],"an":[149],"anchorfree":[150],"CenterNet":[151],"head":[152],"empirical":[154],"loss":[155],"scaling":[156],"are":[157],"integrated":[158],"harmonize":[160],"optimization":[161],"across":[162],"27":[163],"distinct":[164],"subtasks.":[165],"Evaluated":[166],"on":[167],"large-scale":[169],"FMC-UIA":[170],"2026":[171],"benchmark,":[172],"our":[173],"UltraTAR":[174],"establishes":[175],"new":[177],"state-of-the-art,":[178],"outperforming":[179],"official":[181],"baseline":[182],"by":[183],"large":[184],"margins":[185],"(e.g.,":[186],"<tex":[187,195],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[188,196],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$+189":[189],"\\%$</tex>":[190,198],"Detection":[192],"IoU":[193],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$+19":[197],"Segmentation":[200],"DSC)":[201],"demonstrating":[203],"exceptional":[204],"versatility":[205],"analysis.":[209]},"counts_by_year":[],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2026-05-21T00:00:00"}