{"id":"https://openalex.org/W4313413262","doi":"https://doi.org/10.1109/bibm55620.2022.9995270","title":"SQET: Squeeze and Excitation Transformer for High-accuracy Brain Age Estimation","display_name":"SQET: Squeeze and Excitation Transformer for High-accuracy Brain Age Estimation","publication_year":2022,"publication_date":"2022-12-06","ids":{"openalex":"https://openalex.org/W4313413262","doi":"https://doi.org/10.1109/bibm55620.2022.9995270"},"language":"en","primary_location":{"id":"doi:10.1109/bibm55620.2022.9995270","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm55620.2022.9995270","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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/A5110840087","display_name":"Yixiao Hu","orcid":"https://orcid.org/0009-0004-6967-1731"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixiao Hu","raw_affiliation_strings":["University of Chinese Academy of Sciences,School of Computer Science and Technology,Beijing,China,101408"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,School of Computer Science and Technology,Beijing,China,101408","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101749192","display_name":"Haolin Wang","orcid":"https://orcid.org/0000-0002-0477-4973"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haolin Wang","raw_affiliation_strings":["Beihang University,School of Computer Science and Engineering,Beijing,China,100191"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,School of Computer Science and Engineering,Beijing,China,100191","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103162735","display_name":"Baobin Li","orcid":"https://orcid.org/0000-0001-5828-9976"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baobin Li","raw_affiliation_strings":["University of Chinese Academy of Sciences,School of Computer Science and Technology,Beijing,China,101408"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,School of Computer Science and Technology,Beijing,China,101408","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1554","last_page":"1557"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9976000189781189,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9966999888420105,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.7305168509483337},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6944870352745056},{"id":"https://openalex.org/keywords/excitation","display_name":"Excitation","score":0.6417137980461121},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6177734136581421},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5690712332725525},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.49346721172332764},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4869857728481293},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48421740531921387},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.45986509323120117},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4531926214694977},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43175312876701355},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.4306177496910095},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.32215434312820435},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3191893696784973},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18167611956596375},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17417749762535095},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.1337815225124359},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.07289782166481018}],"concepts":[{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.7305168509483337},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6944870352745056},{"id":"https://openalex.org/C83581075","wikidata":"https://www.wikidata.org/wiki/Q1361503","display_name":"Excitation","level":2,"score":0.6417137980461121},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6177734136581421},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5690712332725525},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.49346721172332764},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4869857728481293},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48421740531921387},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.45986509323120117},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4531926214694977},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43175312876701355},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.4306177496910095},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32215434312820435},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3191893696784973},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18167611956596375},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17417749762535095},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.1337815225124359},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.07289782166481018},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm55620.2022.9995270","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm55620.2022.9995270","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 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/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1458229720","https://openalex.org/W1560723556","https://openalex.org/W2093745477","https://openalex.org/W2116967320","https://openalex.org/W2152723280","https://openalex.org/W2167868121","https://openalex.org/W2194775991","https://openalex.org/W2590651237","https://openalex.org/W2752782242","https://openalex.org/W2963420686","https://openalex.org/W2995808388","https://openalex.org/W3037286432","https://openalex.org/W3092783643","https://openalex.org/W3198401109","https://openalex.org/W4206367614","https://openalex.org/W4385245566","https://openalex.org/W6739901393","https://openalex.org/W6772352366","https://openalex.org/W6811046068","https://openalex.org/W6841287799"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2372194214","https://openalex.org/W3081640970"],"abstract_inverted_index":{"The":[0],"aging":[1,24],"process":[2,25],"of":[3,23,59,71,140,149],"human":[4],"brain":[5,12,37,90,134],"is":[6,26,100],"complex,":[7],"which":[8,94,157],"can":[9,145],"result":[10,148],"in":[11,76,93,104,107],"structural":[13],"changes.":[14],"One":[15],"promising":[16],"way":[17],"to":[18,35,51,67,111,166],"gain":[19],"a":[20,48,55,95],"deep":[21],"understanding":[22],"using":[27],"machine":[28],"learning,":[29],"typically":[30],"convolutional":[31],"neural":[32],"network":[33],"(CNN),":[34],"predict":[36],"age":[38,91,138],"based":[39],"on":[40],"magnetic":[41],"resonance":[42],"imaging":[43],"data.":[44],"Though":[45],"CNN":[46],"has":[47,158],"strong":[49],"ability":[50,66],"capture":[52,68,112],"features":[53,70,114],"from":[54],"small":[56],"local":[57],"region":[58],"the":[60,65,72,81,108,147,153],"input":[61],"image,":[62],"it":[63],"lacks":[64],"global":[69,113],"surrounding":[73],"neighbors.":[74],"Thus,":[75],"this":[77],"paper,":[78],"we":[79],"propose":[80],"squeeze":[82,96],"and":[83,97,102,152],"excitation":[84,98],"transformer":[85,109],"(SQET)":[86],"for":[87,127],"pursuing":[88],"high-accuracy":[89],"estimation,":[92],"module":[99],"designed":[101],"fused":[103],"conventional":[105],"self-attention":[106],"structure":[110],"among":[115],"different":[116],"localities":[117],"even":[118],"if":[119],"they":[120],"are":[121],"spatially":[122],"far":[123],"apart.":[124],"In":[125],"particular,":[126],"9":[128],"public":[129],"datasets":[130],"with":[131,136],"6,318":[132],"healthy":[133],"Tl-MRIs":[135],"an":[137],"range":[139],"6-88,":[141],"our":[142],"proposed":[143],"SQET":[144],"achieve":[146],"2.55":[150],"MAE":[151],"correlation":[154],"coefficient":[155],"r=0.983,":[156],"significantly":[159],"outperformed":[160],"all":[161],"other":[162],"reported":[163],"models":[164],"up":[165],"now.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
