{"id":"https://openalex.org/W4402774714","doi":"https://doi.org/10.1109/access.2024.3461754","title":"MetaFormer and CNN Hybrid Model for Polyp Image Segmentation","display_name":"MetaFormer and CNN Hybrid Model for Polyp Image Segmentation","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4402774714","doi":"https://doi.org/10.1109/access.2024.3461754"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3461754","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3461754","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.2024.3461754","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078220315","display_name":"Hyunnam Lee","orcid":"https://orcid.org/0000-0003-0369-8742"},"institutions":[{"id":"https://openalex.org/I4210163483","display_name":"Incheon Medical Center","ror":"https://ror.org/02vygm709","country_code":"KR","type":"healthcare","lineage":["https://openalex.org/I4210163483"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunnam Lee","raw_affiliation_strings":["Incheon International Airport Corporation, Incheon, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-0369-8742","affiliations":[{"raw_affiliation_string":"Incheon International Airport Corporation, Incheon, South Korea","institution_ids":["https://openalex.org/I4210163483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053930340","display_name":"Juhan Yoo","orcid":"https://orcid.org/0000-0002-2329-7375"},"institutions":[{"id":"https://openalex.org/I4210107562","display_name":"Semyung University","ror":"https://ror.org/01d100w34","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210107562"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Juhan Yoo","raw_affiliation_strings":["Department of Computer, Semyung University, Jecheon-si, South Korea","Department of Computer, Semyung University, Jecheon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-2329-7375","affiliations":[{"raw_affiliation_string":"Department of Computer, Semyung University, Jecheon-si, South Korea","institution_ids":["https://openalex.org/I4210107562"]},{"raw_affiliation_string":"Department of Computer, Semyung University, Jecheon, South Korea","institution_ids":["https://openalex.org/I4210107562"]}]}],"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":2.9301,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.91394637,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"12","issue":null,"first_page":"133694","last_page":"133702"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.3971000015735626,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.3971000015735626,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13027","display_name":"Applied Advanced Technologies","score":0.3828999996185303,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7468979954719543},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6701298952102661},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6375516653060913},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5228122472763062},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5148974657058716},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48843374848365784},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4585873782634735}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7468979954719543},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6701298952102661},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6375516653060913},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5228122472763062},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5148974657058716},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48843374848365784},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4585873782634735}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3461754","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3461754","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:da6482d6c3d4429db68121a3001dd863","is_oa":true,"landing_page_url":"https://doaj.org/article/da6482d6c3d4429db68121a3001dd863","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 12, Pp 133694-133702 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3461754","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3461754","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":[{"id":"https://openalex.org/F4320321288","display_name":"Semyung University","ror":"https://ror.org/01d100w34"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2008359794","https://openalex.org/W2111147183","https://openalex.org/W2194775991","https://openalex.org/W2285968993","https://openalex.org/W2560328367","https://openalex.org/W2586952804","https://openalex.org/W2752782242","https://openalex.org/W2997286550","https://openalex.org/W2999580839","https://openalex.org/W3081752372","https://openalex.org/W3094502228","https://openalex.org/W3131500599","https://openalex.org/W3162386519","https://openalex.org/W3177634011","https://openalex.org/W3194513480","https://openalex.org/W3194686611","https://openalex.org/W3198730749","https://openalex.org/W4226068227","https://openalex.org/W4287225564","https://openalex.org/W4289822973","https://openalex.org/W4299652825","https://openalex.org/W4312349930","https://openalex.org/W4380992399","https://openalex.org/W4384517811","https://openalex.org/W4385484654","https://openalex.org/W4388145401","https://openalex.org/W4390168321","https://openalex.org/W4392642839","https://openalex.org/W6713134421","https://openalex.org/W6739696289","https://openalex.org/W6757817989","https://openalex.org/W6769627184","https://openalex.org/W6797399245"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W2501551404","https://openalex.org/W4385583601","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W4395685956","https://openalex.org/W2799953226","https://openalex.org/W4398146871","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Transformer-based":[0],"methods":[1,145],"have":[2,21],"become":[3],"dominant":[4],"in":[5,27,86,133],"the":[6,12,44,72,95,105,147],"medical":[7],"image":[8],"research":[9,41],"field":[10],"since":[11],"Vision":[13],"Transformer":[14],"achieved":[15],"superior":[16],"performance.":[17],"Although":[18],"transformer-based":[19],"approaches":[20],"resolved":[22],"long-range":[23],"dependency":[24],"problems":[25],"inherent":[26],"Convolutional":[28],"Neural":[29],"Network":[30],"(CNN)":[31],"methods,":[32],"they":[33],"struggle":[34],"to":[35,89],"capture":[36],"local":[37,48,91,99],"detail":[38,49],"information.":[39,52],"Recent":[40],"focuses":[42],"on":[43,110,146],"robust":[45],"combination":[46,96],"of":[47,98,108,135],"and":[50,75,83,93,100,122,138],"semantic":[51],"To":[53],"address":[54],"this":[55],"problem,":[56],"we":[57],"propose":[58],"a":[59,77],"novel":[60],"transformer-CNN":[61],"hybrid":[62],"network":[63],"named":[64],"RAPUNet.":[65],"The":[66],"proposed":[67],"approach":[68],"employs":[69],"MetaFormer":[70],"as":[71],"transformer":[73],"backbone":[74],"introduces":[76],"custom":[78],"convolutional":[79],"block,":[80],"RAPU":[81],"(Residual":[82],"Atrous":[84],"Convolution":[85],"Parallel":[87],"Unit),":[88],"enhance":[90],"features":[92],"alleviate":[94],"problem":[97],"global":[101],"features.":[102],"We":[103],"evaluate":[104],"segmentation":[106],"performance":[107,132],"RAPUNet":[109,142],"popular":[111],"benchmarking":[112],"datasets":[113],"for":[114],"polyp":[115],"segmentation,":[116],"including":[117],"Kvasir-SEG,":[118],"CVC-ClinicDB,":[119],"CVC-ColonDB,":[120],"EndoScene-CVC300,":[121],"ETIS-LaribPolypDB.":[123],"Experimental":[124],"results":[125],"show":[126],"that":[127],"our":[128],"model":[129],"achieves":[130],"competitive":[131],"terms":[134],"mean":[136,139],"Dice":[137],"IoU.":[140],"Particularly,":[141],"outperforms":[143],"state-of-the-art":[144],"CVC-ClinicDB":[148],"dataset.":[149],"Code":[150],"available:":[151],"<uri":[152],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[153],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/hyunnamlee/RAPUNet</uri>.":[154]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":15}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}