{"id":"https://openalex.org/W7140321650","doi":"https://doi.org/10.48550/arxiv.2603.23390","title":"Harnessing Lightweight Transformer with Contextual Synergic Enhancement for Efficient 3D Medical Image Segmentation","display_name":"Harnessing Lightweight Transformer with Contextual Synergic Enhancement for Efficient 3D Medical Image Segmentation","publication_year":2026,"publication_date":"2026-03-24","ids":{"openalex":"https://openalex.org/W7140321650","doi":"https://doi.org/10.48550/arxiv.2603.23390"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.23390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23390","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.2603.23390","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130565058","display_name":"Xinyu Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130594668","display_name":"Zhen Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090481419","display_name":"Wuyang Li","orcid":"https://orcid.org/0000-0002-7338-9251"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Wuyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130584236","display_name":"Chenxin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chenxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130599747","display_name":"Yixuan Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Yixuan","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":0.8492000102996826,"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":0.8492000102996826,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.03440000116825104,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.027400000020861626,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6553000211715698},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5942000150680542},{"id":"https://openalex.org/keywords/data-consistency","display_name":"Data consistency","score":0.4602999985218048},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4205999970436096},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.4083000123500824},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.38929998874664307},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.3327000141143799},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.3215999901294708},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3192000091075897}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7466999888420105},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6553000211715698},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5942000150680542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47749999165534973},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.4602999985218048},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4205999970436096},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.4083000123500824},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3327000141143799},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31700000166893005},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31139999628067017},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3061999976634979},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.299699991941452},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2890999913215637},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.289000004529953},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.2879999876022339},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.28459998965263367},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.25940001010894775},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.23390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23390","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.2603.23390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23390","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Transformers":[0],"have":[1],"shown":[2],"remarkable":[3],"performance":[4,162],"in":[5,160],"3D":[6],"medical":[7],"image":[8],"segmentation,":[9],"but":[10],"their":[11,24],"high":[12],"computational":[13],"requirements":[14],"and":[15,37,64,71,163,192],"need":[16],"for":[17,146],"large":[18],"amounts":[19],"of":[20,111,124,157],"labeled":[21,170],"data":[22,38,109,126,171],"limit":[23],"applicability.":[25],"To":[26],"address":[27],"these":[28],"challenges,":[29],"we":[30,41,78,95],"consider":[31],"two":[32],"crucial":[33],"aspects:":[34],"model":[35,50],"efficiency":[36,110],"efficiency.":[39,51,164],"Specifically,":[40],"propose":[42],"Light-UNETR,":[43],"a":[44,54,80,97],"lightweight":[45],"transformer":[46],"designed":[47],"to":[48,86,106,120,140],"achieve":[49],"Light-UNETR":[52],"features":[53,73],"Lightweight":[55],"Dimension":[56],"Reductive":[57],"Attention":[58],"(LIDR)":[59],"module,":[60],"which":[61,104],"reduces":[62],"spatial":[63,143],"channel":[65,89],"dimensions":[66],"while":[67,185],"capturing":[68],"both":[69,161],"global":[70],"local":[72],"via":[74],"multi-branch":[75],"attention.":[76],"Additionally,":[77],"introduce":[79,96],"Compact":[81],"Gated":[82],"Linear":[83],"Unit":[84],"(CGLU)":[85],"selectively":[87],"control":[88],"interaction":[90],"with":[91,127,167],"minimal":[92],"parameters.":[93],"Furthermore,":[94],"Contextual":[98],"Synergic":[99],"Enhancement":[100],"(CSE)":[101],"learning":[102,123],"strategy,":[103],"aims":[105],"boost":[107],"the":[108,116,122,142,155,173,188],"Transformers.":[112],"It":[113],"first":[114],"leverages":[115],"extrinsic":[117],"contextual":[118,138],"information":[119,139],"support":[121],"unlabeled":[125,147],"Attention-Guided":[128],"Replacement,":[129],"then":[130],"applies":[131],"Spatial":[132],"Masking":[133],"Consistency":[134],"that":[135],"utilizes":[136],"intrinsic":[137],"enhance":[141],"context":[144],"reasoning":[145],"data.":[148],"Extensive":[149],"experiments":[150],"on":[151,172],"various":[152],"benchmarks":[153],"demonstrate":[154],"superiority":[156],"our":[158,178],"approach":[159],"For":[165],"example,":[166],"only":[168],"10%":[169],"Left":[174],"Atrial":[175],"Segmentation":[176],"dataset,":[177],"method":[179],"surpasses":[180],"BCP":[181],"by":[182,190,194],"1.43%":[183],"Jaccard":[184],"drastically":[186],"reducing":[187],"FLOPs":[189],"90.8%":[191],"parameters":[193],"85.8%.":[195],"Code":[196],"is":[197],"released":[198],"at":[199],"https://github.com/CUHK-AIM-Group/Light-UNETR.":[200]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-26T00:00:00"}
