{"id":"https://openalex.org/W4406260565","doi":"https://doi.org/10.1109/bibm62325.2024.10822137","title":"A Tiny Efficient U-Net with Gated Linear Attention for Medical Image Segmentation","display_name":"A Tiny Efficient U-Net with Gated Linear Attention for Medical Image Segmentation","publication_year":2024,"publication_date":"2024-12-03","ids":{"openalex":"https://openalex.org/W4406260565","doi":"https://doi.org/10.1109/bibm62325.2024.10822137"},"language":"en","primary_location":{"id":"doi:10.1109/bibm62325.2024.10822137","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10822137","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5108904671","display_name":"Sibo Ju","orcid":"https://orcid.org/0009-0008-6131-8449"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sibo Ju","raw_affiliation_strings":["Fuzhou University,College of Computer and Data Science,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Computer and Data Science,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhaozhen Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaozhen Chen","raw_affiliation_strings":["Fuzhou University,College of Computer and Data Science,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Computer and Data Science,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003401464","display_name":"Xiangwen Liao","orcid":"https://orcid.org/0000-0002-9500-4447"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangwen Liao","raw_affiliation_strings":["Fuzhou University,College of Computer and Data Science,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Computer and Data Science,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072622467","display_name":"Yiqing Shen","orcid":"https://orcid.org/0000-0001-7866-3339"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiqing Shen","raw_affiliation_strings":["Johns Hopkins University,Department of Computer Science,Baltimore,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins University,Department of Computer Science,Baltimore,USA","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056327997","display_name":"Junjun He","orcid":"https://orcid.org/0000-0002-1813-1784"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4391012619","display_name":"Shanghai Artificial Intelligence Laboratory","ror":"https://ror.org/03wkvpx79","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391012619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjun He","raw_affiliation_strings":["Shanghai Artificial Intelligence Laboratory,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Artificial Intelligence Laboratory,Shanghai,China","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4391012619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036020328","display_name":"Yanzhou Su","orcid":"https://orcid.org/0000-0002-3377-469X"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanzhou Su","raw_affiliation_strings":["Fuzhou University,College of Computer and Data Science,Fuzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuzhou University,College of Computer and Data Science,Fuzhou,China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3367","last_page":"3371"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.8422999978065491,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.8422999978065491,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.8325999975204468,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.8317999839782715,"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/image-segmentation","display_name":"Image segmentation","score":0.663579523563385},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.594637930393219},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5396497249603271},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5396151542663574},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5078626275062561},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43594372272491455}],"concepts":[{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.663579523563385},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.594637930393219},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5396497249603271},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5396151542663574},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5078626275062561},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43594372272491455}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm62325.2024.10822137","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10822137","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2395611524","https://openalex.org/W2565639579","https://openalex.org/W2963163009","https://openalex.org/W2963946669","https://openalex.org/W2997286550","https://openalex.org/W4387211614","https://openalex.org/W4389236004","https://openalex.org/W4390971133","https://openalex.org/W4391109864","https://openalex.org/W4400269415","https://openalex.org/W4401725766","https://openalex.org/W6737664043","https://openalex.org/W6750469568","https://openalex.org/W6790275670","https://openalex.org/W6859298233","https://openalex.org/W6861213978","https://openalex.org/W6862363860","https://openalex.org/W6862845145","https://openalex.org/W6863518767","https://openalex.org/W6866741285"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Medical":[0],"image":[1],"segmentation":[2,21,134],"is":[3],"crucial":[4],"for":[5,88,137],"diagnosis":[6],"and":[7,58,75,80,103,127],"treatment":[8],"planning.":[9],"While":[10],"recent":[11],"advancements":[12],"in":[13,26,38],"deep":[14],"learning,":[15],"particularly":[16],"UNet":[17],"variants,":[18],"have":[19],"improved":[20],"performance,":[22],"they":[23],"often":[24],"result":[25],"increased":[27],"model":[28,55],"complexity,":[29],"which":[30],"limits":[31],"their":[32],"real-time":[33,133],"applicability":[34],"on":[35,107],"resource-constrained":[36],"devices":[37],"clinical":[39,139],"settings.":[40],"To":[41],"address":[42],"this":[43],"challenge,":[44],"we":[45],"present":[46],"the":[47],"Tiny":[48],"Efficient":[49],"U-Net":[50],"(TE-UNet),":[51],"a":[52,62,67,122],"novel":[53],"lightweight":[54],"balancing":[56],"efficiency":[57,92],"accuracy.":[59],"TE-UNet":[60,116,131],"uses":[61],"U-shaped":[63],"encoder-decoder":[64],"framework":[65],"with":[66],"gated":[68],"linear":[69],"attention":[70],"mechanism":[71],"to":[72],"process":[73],"low-level":[74],"high-level":[76],"features,":[77],"preserving":[78],"details":[79],"reducing":[81],"complexity.":[82],"It":[83],"employs":[84],"depth-wise":[85],"separable":[86],"convolutions":[87],"higher-level":[89],"processing,":[90],"enhancing":[91],"without":[93],"losing":[94],"performance.":[95],"Additionally,":[96],"skip":[97],"connections":[98],"improve":[99],"multi-scale":[100],"feature":[101],"extraction":[102],"information":[104],"flow.":[105],"Experiments":[106],"two":[108],"public":[109],"datasets":[110],"across":[111],"different":[112],"modalities":[113],"demonstrate":[114],"that":[115],"outperforms":[117],"ten":[118],"state-of-the-art":[119],"methods,":[120],"maintaining":[121],"parameter":[123],"size":[124],"under":[125],"40KB":[126],"low":[128],"computational":[129],"cost.":[130],"makes":[132],"more":[135],"accessible":[136],"various":[138],"applications.":[140]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}