{"id":"https://openalex.org/W7127182684","doi":"https://doi.org/10.1109/access.2026.3660056","title":"MEAA-Net: Memory-Efficient Asymmetric Attention for Resource-Constrained Lung Nodule Classification","display_name":"MEAA-Net: Memory-Efficient Asymmetric Attention for Resource-Constrained Lung Nodule Classification","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7127182684","doi":"https://doi.org/10.1109/access.2026.3660056"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3660056","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660056","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3660056","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124785016","display_name":"Lan Qiao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210142644","display_name":"Intel (Malaysia)","ror":"https://ror.org/048jw1p35","country_code":"MY","type":"company","lineage":["https://openalex.org/I1343180700","https://openalex.org/I4210142644"]},{"id":"https://openalex.org/I4576418","display_name":"University of Technology Malaysia","ror":"https://ror.org/026w31v75","country_code":"MY","type":"education","lineage":["https://openalex.org/I4576418"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Lan Qiao","raw_affiliation_strings":["Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia"],"raw_orcid":"https://orcid.org/0009-0007-2270-231X","affiliations":[{"raw_affiliation_string":"Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia","institution_ids":["https://openalex.org/I4576418","https://openalex.org/I4210142644"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031274277","display_name":"Suriayati Chuprat","orcid":null},"institutions":[{"id":"https://openalex.org/I4210142644","display_name":"Intel (Malaysia)","ror":"https://ror.org/048jw1p35","country_code":"MY","type":"company","lineage":["https://openalex.org/I1343180700","https://openalex.org/I4210142644"]},{"id":"https://openalex.org/I4576418","display_name":"University of Technology Malaysia","ror":"https://ror.org/026w31v75","country_code":"MY","type":"education","lineage":["https://openalex.org/I4576418"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Suriayati Chuprat","raw_affiliation_strings":["Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia","institution_ids":["https://openalex.org/I4576418","https://openalex.org/I4210142644"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.129616,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"18099","last_page":"18114"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.8855000138282776,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.8855000138282776,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.03790000081062317,"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"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.006800000090152025,"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/inference","display_name":"Inference","score":0.6358000040054321},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5267999768257141},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4634999930858612},{"id":"https://openalex.org/keywords/nodule","display_name":"Nodule (geology)","score":0.4629000127315521},{"id":"https://openalex.org/keywords/lung-cancer","display_name":"Lung cancer","score":0.4553000032901764},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44609999656677246},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.4277999997138977},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4260999858379364}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.705299973487854},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6358000040054321},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.569599986076355},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5267999768257141},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4634999930858612},{"id":"https://openalex.org/C2776731575","wikidata":"https://www.wikidata.org/wiki/Q2916245","display_name":"Nodule (geology)","level":2,"score":0.4629000127315521},{"id":"https://openalex.org/C2776256026","wikidata":"https://www.wikidata.org/wiki/Q47912","display_name":"Lung cancer","level":2,"score":0.4553000032901764},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44609999656677246},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.4277999997138977},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4260999858379364},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.42570000886917114},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34209999442100525},{"id":"https://openalex.org/C2777714996","wikidata":"https://www.wikidata.org/wiki/Q7886","display_name":"Lung","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C44648626","wikidata":"https://www.wikidata.org/wiki/Q1049848","display_name":"Percentage point","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30079999566078186},{"id":"https://openalex.org/C2989236134","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Patient care","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C3019813237","wikidata":"https://www.wikidata.org/wiki/Q65089264","display_name":"Model validation","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C2983914783","wikidata":"https://www.wikidata.org/wiki/Q3286546","display_name":"Lung disease","level":3,"score":0.2660999894142151},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3660056","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660056","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:6045e34ac3574f868a05a33986be8361","is_oa":true,"landing_page_url":"https://doaj.org/article/6045e34ac3574f868a05a33986be8361","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 18099-18114 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3660056","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3660056","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W130099911","https://openalex.org/W1901129140","https://openalex.org/W2194775991","https://openalex.org/W2524399695","https://openalex.org/W2584017349","https://openalex.org/W2626997132","https://openalex.org/W2752782242","https://openalex.org/W2798170643","https://openalex.org/W2946185430","https://openalex.org/W2963122961","https://openalex.org/W2982083293","https://openalex.org/W2996092187","https://openalex.org/W3105966348","https://openalex.org/W3128646645","https://openalex.org/W3138516171","https://openalex.org/W3204166336","https://openalex.org/W3211983116","https://openalex.org/W4212875960","https://openalex.org/W4220731986","https://openalex.org/W4312468136","https://openalex.org/W4312847199","https://openalex.org/W4321232185","https://openalex.org/W4327941939","https://openalex.org/W4388636231","https://openalex.org/W4391109864","https://openalex.org/W4399039725","https://openalex.org/W4400881081","https://openalex.org/W4402646954","https://openalex.org/W4404821541","https://openalex.org/W4407128430"],"related_works":[],"abstract_inverted_index":{"Early":[0],"detection":[1],"and":[2,48,69,80,89,106,119,162,186],"classification":[3,170],"of":[4,24,86,181],"lung":[5,11,168],"nodules":[6],"is":[7],"critical":[8],"for":[9,178],"improving":[10],"cancer":[12],"prognosis,":[13],"yet":[14],"existing":[15],"deep":[16],"learning":[17],"models":[18],"often":[19],"exceed":[20],"the":[21,83,87,154,176],"memory":[22,56],"budgets":[23],"typical":[25],"clinical":[26,173],"deployment":[27],"hardware,":[28],"hindering":[29],"real-time":[30],"deployment.":[31],"We":[32],"introduce":[33],"MEAA-Net,":[34],"a":[35,77,127],"memory-efficient":[36],"asymmetric":[37],"attention":[38],"network":[39],"that":[40],"processes":[41],"intermediate":[42],"feature":[43],"maps":[44],"into":[45],"batch-wise":[46],"partitions":[47],"leverages":[49],"gradient":[50],"checkpointing":[51],"to":[52,65,71,111],"reduce":[53],"peak":[54],"training":[55],"requirements.":[57],"Five":[58],"architectural":[59],"variants":[60],"(ranging":[61],"from":[62],"61":[63],"K":[64,97],"14.65":[66],"million":[67,117],"parameters":[68,118],"0.76":[70,99],"3.73":[72],"GFLOPs)":[73,100],"were":[74],"trained":[75],"under":[76,153],"unified":[78],"protocol":[79],"evaluated":[81],"on":[82,104,108,126],"validation":[84,147],"splits":[85],"LUNA16":[88,105],"LIDC-IDRI":[90],"public":[91],"datasets.":[92],"Our":[93],"smallest":[94],"model":[95],"(61":[96],"parameters;":[98],"achieved":[101],"80.8%":[102],"accuracy":[103,114,148],"65.4%":[107],"LIDC-IDRI,":[109],"corresponding":[110],"about":[112],"12":[113],"points":[115,152],"per":[116],"delivering":[120],"single-slice":[121],"inference":[122],"in":[123,171,184],"11":[124],"ms":[125],"standard":[128],"CPU.":[129],"Compared":[130],"with":[131],"ResNet-18":[132],"(11.24":[133],"M":[134],"parameters),":[135],"this":[136],"variant":[137],"reduces":[138],"parameter":[139],"count":[140],"by":[141],"184":[142],"\u00d7":[143],"while":[144],"keeping":[145],"mean":[146],"within":[149],"2":[150],"percentage":[151],"same":[155],"evaluation":[156],"protocol.":[157],"MEAA-Net\u2019s":[158],"highly":[159],"compact":[160],"design":[161],"competitive":[163],"performance":[164],"enable":[165],"real-time,":[166],"hardware-friendly":[167],"nodule":[169],"resource-constrained":[172],"environments,":[174],"paving":[175],"way":[177],"broader":[179],"adoption":[180],"AI-assisted":[182],"diagnostics":[183],"primary":[185],"community":[187],"care":[188],"settings.":[189]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-03T00:00:00"}
