{"id":"https://openalex.org/W4385486267","doi":"https://doi.org/10.1109/access.2023.3301160","title":"Efficient Scopeformer: Toward Scalable and Rich Feature Extraction for Intracranial Hemorrhage Detection","display_name":"Efficient Scopeformer: Toward Scalable and Rich Feature Extraction for Intracranial Hemorrhage Detection","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4385486267","doi":"https://doi.org/10.1109/access.2023.3301160"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3301160","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3301160","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10201867.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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://ieeexplore.ieee.org/ielx7/6287639/6514899/10201867.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062405419","display_name":"Yassine Barhoumi","orcid":"https://orcid.org/0000-0003-4451-5930"},"institutions":[{"id":"https://openalex.org/I44265643","display_name":"Rowan University","ror":"https://ror.org/049v69k10","country_code":"US","type":"education","lineage":["https://openalex.org/I44265643"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yassine Barhoumi","raw_affiliation_strings":["Electrical and Computer Science Department, Rowan University, Glassboro, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Science Department, Rowan University, Glassboro, NJ, USA","institution_ids":["https://openalex.org/I44265643"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041401460","display_name":"Nidhal Bouaynaya","orcid":"https://orcid.org/0000-0002-8833-8414"},"institutions":[{"id":"https://openalex.org/I44265643","display_name":"Rowan University","ror":"https://ror.org/049v69k10","country_code":"US","type":"education","lineage":["https://openalex.org/I44265643"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nidhal Carla Bouaynaya","raw_affiliation_strings":["Electrical and Computer Science Department, Rowan University, Glassboro, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Science Department, Rowan University, Glassboro, NJ, USA","institution_ids":["https://openalex.org/I44265643"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045996251","display_name":"Ghulam Rasool","orcid":"https://orcid.org/0000-0001-8551-0090"},"institutions":[{"id":"https://openalex.org/I3019308854","display_name":"Moffitt Cancer Center","ror":"https://ror.org/01xf75524","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I3019308854"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ghulam Rasool","raw_affiliation_strings":["Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA","Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States"],"affiliations":[{"raw_affiliation_string":"Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA","institution_ids":["https://openalex.org/I3019308854"]},{"raw_affiliation_string":"Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States","institution_ids":["https://openalex.org/I3019308854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5062405419"],"corresponding_institution_ids":["https://openalex.org/I44265643"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.0353,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.87478049,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"11","issue":null,"first_page":"81656","last_page":"81671"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11763","display_name":"Intracerebral and Subarachnoid Hemorrhage Research","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Neurology"},"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/T11763","display_name":"Intracerebral and Subarachnoid Hemorrhage Research","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Neurology"},"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.989300012588501,"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/T10227","display_name":"Acute Ischemic Stroke Management","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/computer-science","display_name":"Computer science","score":0.8239952325820923},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6926149725914001},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6182667016983032},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5945225358009338},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5762324929237366},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5760005712509155},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.49582919478416443},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48888105154037476},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.417390912771225},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3200720250606537}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8239952325820923},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6926149725914001},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6182667016983032},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5945225358009338},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5762324929237366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5760005712509155},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.49582919478416443},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48888105154037476},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.417390912771225},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3200720250606537},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2023.3301160","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3301160","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10201867.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:rdw.rowan.edu:engineering_facpub-1267","is_oa":true,"landing_page_url":"https://rdw.rowan.edu/engineering_facpub/268","pdf_url":null,"source":{"id":"https://openalex.org/S4377196130","display_name":"Rowan Digitals Works (Rowan University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I44265643","host_organization_name":"Rowan University","host_organization_lineage":["https://openalex.org/I44265643"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Henry M. Rowan College of Engineering Departmental Research","raw_type":"article"},{"id":"pmh:oai:doaj.org/article:bff6ace16fb9498787579ecce5576847","is_oa":true,"landing_page_url":"https://doaj.org/article/bff6ace16fb9498787579ecce5576847","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 81656-81671 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3301160","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3301160","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10201867.pdf","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G2189403844","display_name":null,"funder_award_id":"ECCS-1903466","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2296916099","display_name":null,"funder_award_id":"ECCS-1","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3026169261","display_name":null,"funder_award_id":"1903466","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3042881226","display_name":"SCenE - Self-Assessment and Continual Learning on Edge Devices","funder_award_id":"2008690","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6136189169","display_name":null,"funder_award_id":"ECCS-1903466","funder_id":"https://openalex.org/F4320337392","funder_display_name":"Division of Electrical, Communications and Cyber Systems"},{"id":"https://openalex.org/G7355102580","display_name":null,"funder_award_id":"OAC-2008690","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337392","display_name":"Division of Electrical, Communications and Cyber Systems","ror":"https://ror.org/01krpsy48"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385486267.pdf","grobid_xml":"https://content.openalex.org/works/W4385486267.grobid-xml"},"referenced_works_count":75,"referenced_works":["https://openalex.org/W2988963","https://openalex.org/W1661149683","https://openalex.org/W1686810756","https://openalex.org/W2013226933","https://openalex.org/W2016313651","https://openalex.org/W2040842434","https://openalex.org/W2097117768","https://openalex.org/W2117539524","https://openalex.org/W2155070090","https://openalex.org/W2161844374","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2302255633","https://openalex.org/W2531409750","https://openalex.org/W2752782242","https://openalex.org/W2797594039","https://openalex.org/W2809254203","https://openalex.org/W2888029456","https://openalex.org/W2896817483","https://openalex.org/W2902218850","https://openalex.org/W2913705661","https://openalex.org/W2955425717","https://openalex.org/W2961934740","https://openalex.org/W2962949934","https://openalex.org/W2963091558","https://openalex.org/W2963446712","https://openalex.org/W2963920537","https://openalex.org/W2966715458","https://openalex.org/W2968124245","https://openalex.org/W2969667256","https://openalex.org/W2975357369","https://openalex.org/W2981413347","https://openalex.org/W2991229825","https://openalex.org/W3000658149","https://openalex.org/W3003299879","https://openalex.org/W3037784242","https://openalex.org/W3089635656","https://openalex.org/W3096609285","https://openalex.org/W3129841412","https://openalex.org/W3137963805","https://openalex.org/W3138516171","https://openalex.org/W3158002704","https://openalex.org/W3178043184","https://openalex.org/W3181733123","https://openalex.org/W3188199774","https://openalex.org/W3188404242","https://openalex.org/W3196045517","https://openalex.org/W4211005142","https://openalex.org/W4226439291","https://openalex.org/W4232415764","https://openalex.org/W4287367114","https://openalex.org/W4308831279","https://openalex.org/W4312269593","https://openalex.org/W4319921239","https://openalex.org/W4385245566","https://openalex.org/W4385258623","https://openalex.org/W6637373629","https://openalex.org/W6640174519","https://openalex.org/W6654777605","https://openalex.org/W6684089368","https://openalex.org/W6684191040","https://openalex.org/W6725739302","https://openalex.org/W6739901393","https://openalex.org/W6750996985","https://openalex.org/W6756444276","https://openalex.org/W6762718338","https://openalex.org/W6766904570","https://openalex.org/W6767853649","https://openalex.org/W6778485988","https://openalex.org/W6779809370","https://openalex.org/W6791705549","https://openalex.org/W6794229253","https://openalex.org/W6798389476","https://openalex.org/W6810751522","https://openalex.org/W7018845661"],"related_works":["https://openalex.org/W2389214306","https://openalex.org/W4293226380","https://openalex.org/W4235240664","https://openalex.org/W2965083567","https://openalex.org/W1838576100","https://openalex.org/W2095886385","https://openalex.org/W2889616422","https://openalex.org/W2089704382","https://openalex.org/W1983399550","https://openalex.org/W2964954556"],"abstract_inverted_index":{"The":[0,58],"quality":[1],"and":[2,13,63,78,93,138,168,197,252],"richness":[3,259],"of":[4,99,117,166,195,202,212,222,250],"feature":[5,84,123,181,185,258],"maps":[6],"extracted":[7],"by":[8,232],"convolution":[9],"neural":[10],"networks":[11],"(CNNs)":[12],"vision":[14,265],"Transformers":[15],"(ViTs)":[16],"directly":[17],"relate":[18],"to":[19,87,94,114,183,216],"the":[20,43,72,96,109,115,122,130,148,210,217,223,229,246,256],"robust":[21],"model":[22,48,110,149,191,226],"performance.":[23],"In":[24,240],"medical":[25,263],"computer":[26,264],"vision,":[27],"these":[28],"information-rich":[29],"features":[30,77,92],"are":[31],"crucial":[32],"for":[33,49,133,260],"detecting":[34],"rare":[35],"cases":[36],"within":[37],"large":[38],"datasets.":[39],"This":[40],"work":[41,243],"presents":[42],"\"Scopeformer,\"":[44],"a":[45,198],"novel":[46],"multi-CNN-ViT":[47],"intracranial":[50],"hemorrhage":[51],"classification":[52],"in":[53,121,147,209],"computed":[54],"tomography":[55],"(CT)":[56],"images.":[57],"Scopeformer":[59,105,160,190,225],"architecture":[60],"is":[61,112],"scalable":[62],"modular,":[64],"which":[65],"allows":[66],"utilizing":[67],"various":[68,104,152],"CNN":[69,178],"architectures":[70,179,248],"as":[71,180],"backbone":[73],"with":[74,103,162,204,236],"diversified":[75],"output":[76,169],"pre-training":[79,131,136],"strategies.":[80],"We":[81],"propose":[82,157],"effective":[83],"projection":[85],"methods":[86],"reduce":[88],"redundancies":[89],"among":[90],"CNN-generated":[91],"control":[95],"input":[97,167],"size":[98],"ViTs.":[100],"Extensive":[101],"experiments":[102],"models":[106],"show":[107],"that":[108,245],"performance":[111,150,238],"proportional":[113],"number":[116,211],"convolutional":[118],"blocks":[119],"employed":[120],"extractor.":[124],"Using":[125],"multiple":[126],"strategies,":[127],"including":[128],"diversifying":[129],"paradigms":[132],"CNNs,":[134],"different":[135,164,176],"datasets,":[137],"style":[139],"transfer":[140],"techniques,":[141],"we":[142,156],"demonstrate":[143],"an":[144,193,205],"overall":[145],"improvement":[146],"at":[151],"computational":[153],"budgets.":[154],"Later,":[155],"smaller":[158],"compute-efficient":[159],"versions":[161],"three":[163],"types":[165],"ViT":[170],"configurations.":[171],"Efficient":[172,189,224],"Scopeformers":[173],"use":[174],"four":[175],"pre-trained":[177],"extractors":[182],"increase":[184],"richness.":[186],"Our":[187],"best":[188],"achieved":[192],"accuracy":[194],"96.94%":[196],"weighted":[199],"logarithmic":[200],"loss":[201],"0.083":[203],"eight":[206],"times":[207,235],"reduction":[208],"trainable":[213],"parameters":[214],"compared":[215],"base":[218],"Scopeformer.":[219],"Another":[220],"version":[221],"further":[227],"reduced":[228],"parameter":[230],"space":[231],"almost":[233],"17":[234],"negligible":[237],"reduction.":[239],"summary,":[241],"our":[242],"showed":[244],"hybrid":[247],"consisting":[249],"CNNs":[251],"ViTs":[253],"might":[254],"provide":[255],"desired":[257],"developing":[261],"accurate":[262],"models.":[266]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2023-08-03T00:00:00"}