{"id":"https://openalex.org/W7138353208","doi":"https://doi.org/10.1609/aaai.v40i4.37292","title":"DeNAS-ViT: Data Efficient NAS-Optimized Vision Transformer for Ultrasound Image Segmentation","display_name":"DeNAS-ViT: Data Efficient NAS-Optimized Vision Transformer for Ultrasound Image Segmentation","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138353208","doi":"https://doi.org/10.1609/aaai.v40i4.37292"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i4.37292","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i4.37292","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37292/41254","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37292/41254","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114081733","display_name":"Renqi Chen","orcid":"https://orcid.org/0009-0001-6855-7900"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Renqi Chen","raw_affiliation_strings":["Fudan University"],"affiliations":[{"raw_affiliation_string":"Fudan University","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129659804","display_name":"Xinzhe Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Xinzhe Zheng","raw_affiliation_strings":["National University of Singapore"],"affiliations":[{"raw_affiliation_string":"National University of Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129710176","display_name":"Haoyang Su","orcid":null},"institutions":[{"id":"https://openalex.org/I5681781","display_name":"The University of Adelaide","ror":"https://ror.org/00892tw58","country_code":"AU","type":"education","lineage":["https://openalex.org/I5681781"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Haoyang Su","raw_affiliation_strings":["University of Adelaide"],"affiliations":[{"raw_affiliation_string":"University of Adelaide","institution_ids":["https://openalex.org/I5681781"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129681004","display_name":"Kehan Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kehan Wu","raw_affiliation_strings":["Southern University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology","institution_ids":["https://openalex.org/I3045169105"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5114081733"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":88.0,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":1.0,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"40","issue":"4","first_page":"3002","last_page":"3010"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.34540000557899475,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.34540000557899475,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.1606999933719635,"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/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.12080000340938568,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/segmentation","display_name":"Segmentation","score":0.6082000136375427},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5950999855995178},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5234000086784363},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4830999970436096},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4415000081062317},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.40220001339912415},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.38920000195503235},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3573000133037567},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3495999872684479}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7483000159263611},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6888999938964844},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6082000136375427},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5950999855995178},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5234000086784363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49939998984336853},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4830999970436096},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4415000081062317},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41290000081062317},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38920000195503235},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.29679998755455017},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27630001306533813},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.2752000093460083},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.2671000063419342},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.2597000002861023},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.2574999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i4.37292","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i4.37292","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37292/41254","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i4.37292","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i4.37292","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37292/41254","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138353208.pdf","grobid_xml":"https://content.openalex.org/works/W7138353208.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"segmentation":[1,81],"of":[2],"ultrasound":[3,79,181],"images":[4],"is":[5,12,47],"essential":[6],"for":[7,78],"reliable":[8],"medical":[9],"diagnoses":[10],"but":[11],"challenged":[13],"by":[14,82],"poor":[15],"image":[16,80],"quality":[17],"and":[18,49,112,123,142],"scarce":[19],"labeled":[20,172],"data.":[21,173],"Prior":[22],"approaches":[23],"have":[24],"relied":[25],"on":[26,54,159],"manually":[27],"designed,":[28],"complex":[29],"network":[30,140],"architectures":[31],"to":[32,52,72,103],"improve":[33],"multi-scale":[34,99],"feature":[35],"extraction.":[36],"However,":[37],"such":[38],"handcrafted":[39],"models":[40],"offer":[41],"limited":[42],"gains":[43],"when":[44],"prior":[45,102],"knowledge":[46],"inadequate":[48],"are":[50],"prone":[51],"overfitting":[53],"small":[55],"datasets.":[56],"In":[57],"this":[58],"paper,":[59],"we":[60,91,128,175],"introduce":[61],"DeNAS-ViT,":[62],"a":[63,131,146],"Data":[64],"efficient":[65,94],"NAS-optimized":[66],"Vision":[67],"Transformer,":[68],"the":[69,104],"first":[70],"method":[71],"leverage":[73],"neural":[74],"architecture":[75,86],"search":[76,101],"(NAS)":[77],"automatically":[83],"optimizing":[84],"model":[85,152],"through":[87],"token-level":[88],"search.":[89],"Specifically,":[90],"propose":[92],"an":[93],"NAS":[95],"module":[96],"that":[97,163],"performs":[98],"token":[100],"ViT\u2019s":[105],"attention":[106],"mechanism,":[107],"effectively":[108],"capturing":[109],"both":[110],"contextual":[111],"local":[113],"features":[114],"while":[115],"minimizing":[116],"computational":[117],"costs.":[118],"Given":[119],"ultrasound\u2019s":[120],"data":[121,126],"scarcity":[122],"NAS\u2019s":[124],"inherent":[125],"demands,":[127],"further":[129],"develop":[130],"NAS-guided":[132],"semi-supervised":[133],"learning":[134,144],"(SSL)":[135],"framework.":[136],"This":[137],"approach":[138],"integrates":[139],"independence":[141],"contrastive":[143],"within":[145],"stage-wise":[147],"optimization":[148],"strategy,":[149],"significantly":[150],"enhancing":[151],"robustness":[153,169],"under":[154],"limited-data":[155],"conditions.":[156],"Extensive":[157],"experiments":[158],"public":[160],"datasets":[161],"demonstrate":[162],"DeNAS-ViT":[164],"achieves":[165],"state-of-the-art":[166],"performance,":[167],"maintaining":[168],"with":[170],"minimal":[171],"Moreover,":[174],"highlight":[176],"DeNAS-ViT\u2019s":[177],"generalization":[178],"potential":[179],"beyond":[180],"imaging,":[182],"underscoring":[183],"its":[184],"broader":[185],"applicability.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-04-15T08:11:43.952461","created_date":"2026-03-18T00:00:00"}
