{"id":"https://openalex.org/W7154222508","doi":"https://doi.org/10.48550/arxiv.2604.11164","title":"RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation","display_name":"RADA: Region-Aware Dual-encoder Auxiliary learning for Barely-supervised Medical Image Segmentation","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154222508","doi":"https://doi.org/10.48550/arxiv.2604.11164"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11164","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11164","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.2604.11164","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133615661","display_name":"Shuang Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Shuang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108845330","display_name":"Boxu Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Boxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133610399","display_name":"Lei Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104228976","display_name":"Xinliang Zhang","orcid":"https://orcid.org/0000-0002-8364-1254"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xinliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133564612","display_name":"Jiakui Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jiakui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133608918","display_name":"Zhengjian Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Zhengjian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133574196","display_name":"Yuanwei Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yuanwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046637756","display_name":"Ye L\u00fc","orcid":"https://orcid.org/0000-0002-2319-7681"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yuxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133605890","display_name":"Yanye Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yanye","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/T10036","display_name":"Advanced Neural Network Applications","score":0.6068000197410583,"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":0.6068000197410583,"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":0.09969999641180038,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.046300001442432404,"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/semantics","display_name":"Semantics (computer science)","score":0.6858000159263611},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6234999895095825},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.6162999868392944},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6000999808311462},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5390999913215637},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.42649999260902405},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4221999943256378},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4187999963760376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.76910001039505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7200999855995178},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6858000159263611},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6234999895095825},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.6162999868392944},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6000999808311462},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5390999913215637},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.42649999260902405},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4187999963760376},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3513000011444092},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C199579030","wikidata":"https://www.wikidata.org/wiki/Q2851778","display_name":"Automatic image annotation","level":4,"score":0.34450000524520874},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3393000066280365},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33239999413490295},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.2849999964237213},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2824000120162964},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2644999921321869}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11164","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11164","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.2604.11164","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11164","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":{"Deep":[0],"learning":[1,31,104],"has":[2],"greatly":[3],"advanced":[4],"medical":[5,71],"image":[6,72],"segmentation,":[7],"but":[8,59],"its":[9],"success":[10],"relies":[11],"heavily":[12],"on":[13,85,112,164],"fully":[14],"supervised":[15],"learning,":[16],"which":[17,106],"requires":[18],"dense":[19],"annotations":[20,49],"that":[21,142],"are":[22],"costly":[23],"and":[24,124,146,167],"time-consuming":[25],"for":[26],"3D":[27],"volumetric":[28],"scans.":[29],"Barely-supervised":[30],"reduces":[32],"annotation":[33,162],"burden":[34],"by":[35,94],"using":[36],"only":[37],"a":[38,76,99,108,151],"few":[39],"labeled":[40],"slices":[41,52],"per":[42],"volume.":[43],"Existing":[44],"methods":[45],"typically":[46],"propagate":[47],"sparse":[48,161],"to":[50,56,114],"unlabeled":[51],"through":[53],"geometric":[54],"continuity":[55],"generate":[57],"pseudo-labels,":[58],"this":[60],"strategy":[61],"lacks":[62],"semantic":[63,136,140],"understanding,":[64],"often":[65],"resulting":[66],"in":[67],"low-quality":[68],"pseudo-labels.":[69],"Furthermore,":[70],"segmentation":[73],"is":[74],"inherently":[75],"pixel-level":[77,147],"visual":[78,91,118,132],"understanding":[79],"task,":[80],"where":[81],"accuracy":[82],"fundamentally":[83],"depends":[84],"the":[86,121],"quality":[87],"of":[88],"local,":[89],"fine-grained":[90,131],"features.":[92],"Inspired":[93],"this,":[95],"we":[96],"propose":[97],"RADA,":[98],"novel":[100],"Region-Aware":[101],"Dual-encoder":[102],"Auxiliary":[103],"pipeline":[105],"introduces":[107],"dual-encoder":[109],"framework":[110,128],"pre-trained":[111],"Alpha-CLIP":[113],"extract":[115],"fine-grained,":[116],"region-specific":[117],"features":[119,133],"from":[120],"original":[122],"images":[123],"limited":[125],"annotations.":[126],"The":[127],"combines":[129],"image-level":[130,144],"with":[134],"text-level":[135],"guidance,":[137],"providing":[138],"region-aware":[139],"supervision":[141],"bridges":[143],"semantics":[145],"segmentation.":[148],"Integrated":[149],"into":[150],"triple-view":[152],"training":[153],"framework,":[154],"RADA":[155],"achieves":[156],"SOTA":[157],"performance":[158],"under":[159],"extremely":[160],"settings":[163],"LA2018,":[165],"KiTS19":[166],"LiTS,":[168],"demonstrating":[169],"robust":[170],"generalization":[171],"across":[172],"diverse":[173],"datasets.":[174]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-15T00:00:00"}