{"id":"https://openalex.org/W4210465915","doi":"https://doi.org/10.1093/jcde/qwac018","title":"SwinE-Net: hybrid deep learning approach to novel polyp segmentation using convolutional neural network and Swin Transformer","display_name":"SwinE-Net: hybrid deep learning approach to novel polyp segmentation using convolutional neural network and Swin Transformer","publication_year":2022,"publication_date":"2022-02-03","ids":{"openalex":"https://openalex.org/W4210465915","doi":"https://doi.org/10.1093/jcde/qwac018"},"language":"en","primary_location":{"id":"doi:10.1093/jcde/qwac018","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwac018","pdf_url":"https://academic.oup.com/jcde/article-pdf/9/2/616/43298266/qwac018.pdf","source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://academic.oup.com/jcde/article-pdf/9/2/616/43298266/qwac018.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042382229","display_name":"Kyeong-Beom Park","orcid":"https://orcid.org/0000-0003-4737-730X"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyeong-Beom Park","raw_affiliation_strings":["Department of Industrial Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, South Korea","institution_ids":["https://openalex.org/I111277659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101767652","display_name":"Jae Yeol Lee","orcid":"https://orcid.org/0000-0002-2653-0742"},"institutions":[{"id":"https://openalex.org/I111277659","display_name":"Chonnam National University","ror":"https://ror.org/05kzjxq56","country_code":"KR","type":"education","lineage":["https://openalex.org/I111277659"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jae Yeol Lee","raw_affiliation_strings":["Department of Industrial Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, Gwangju 61186, South Korea","institution_ids":["https://openalex.org/I111277659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101767652"],"corresponding_institution_ids":["https://openalex.org/I111277659"],"apc_list":{"value":1650,"currency":"USD","value_usd":1650},"apc_paid":{"value":1650,"currency":"USD","value_usd":1650},"fwci":9.6383,"has_fulltext":true,"cited_by_count":101,"citation_normalized_percentile":{"value":0.98784008,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"9","issue":"2","first_page":"616","last_page":"632"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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/T10552","display_name":"Colorectal Cancer Screening and Detection","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"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":0.9969000220298767,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9925000071525574,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7839587926864624},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6790887117385864},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6772928833961487},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5974051356315613},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.586952269077301},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5676367878913879},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5600714683532715},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5501573085784912},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4425816833972931},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.42393189668655396}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7839587926864624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6790887117385864},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6772928833961487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5974051356315613},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.586952269077301},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5676367878913879},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5600714683532715},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5501573085784912},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4425816833972931},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.42393189668655396},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","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},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1093/jcde/qwac018","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwac018","pdf_url":"https://academic.oup.com/jcde/article-pdf/9/2/616/43298266/qwac018.pdf","source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1093/jcde/qwac018","is_oa":true,"landing_page_url":"https://doi.org/10.1093/jcde/qwac018","pdf_url":"https://academic.oup.com/jcde/article-pdf/9/2/616/43298266/qwac018.pdf","source":{"id":"https://openalex.org/S2485147958","display_name":"Journal of Computational Design and Engineering","issn_l":"2288-4300","issn":["2288-4300","2288-5048"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational Design and Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6600000262260437,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1156133555","display_name":null,"funder_award_id":"2019R1I1A3A01059082","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G7855437934","display_name":null,"funder_award_id":"HI19C0642","funder_id":"https://openalex.org/F4320322107","funder_display_name":"Korea Health Industry Development Institute"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322107","display_name":"Korea Health Industry Development Institute","ror":"https://ror.org/00fdzyk40"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4210465915.pdf","grobid_xml":"https://content.openalex.org/works/W4210465915.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W2008359794","https://openalex.org/W2021088830","https://openalex.org/W2063979222","https://openalex.org/W2285968993","https://openalex.org/W2560328367","https://openalex.org/W2774320778","https://openalex.org/W2884436604","https://openalex.org/W2890084211","https://openalex.org/W2914444914","https://openalex.org/W2963529609","https://openalex.org/W2981035911","https://openalex.org/W2986785750","https://openalex.org/W2997286550","https://openalex.org/W2999580839","https://openalex.org/W3009963727","https://openalex.org/W3026290486","https://openalex.org/W3027763298","https://openalex.org/W3041409972","https://openalex.org/W3048209253","https://openalex.org/W3088226460","https://openalex.org/W3100548413","https://openalex.org/W3124994365","https://openalex.org/W3127751679","https://openalex.org/W3131151690","https://openalex.org/W3138516171","https://openalex.org/W3148874463","https://openalex.org/W3151241602","https://openalex.org/W3153906112","https://openalex.org/W3160284783","https://openalex.org/W3174540415","https://openalex.org/W3189898414","https://openalex.org/W3204166336","https://openalex.org/W6639824700","https://openalex.org/W6640054144","https://openalex.org/W6649172765","https://openalex.org/W6687483927","https://openalex.org/W6694260854","https://openalex.org/W6725739302","https://openalex.org/W6734551214","https://openalex.org/W6742002635","https://openalex.org/W6746123559","https://openalex.org/W6753412334","https://openalex.org/W6761741274","https://openalex.org/W6762718338","https://openalex.org/W6770002327","https://openalex.org/W6772551961","https://openalex.org/W6780102755","https://openalex.org/W6787674418","https://openalex.org/W6788135285","https://openalex.org/W6789459963","https://openalex.org/W6790275670","https://openalex.org/W6793746569","https://openalex.org/W6794220449","https://openalex.org/W6795435739"],"related_works":["https://openalex.org/W2381242807","https://openalex.org/W3126131230","https://openalex.org/W2347541121","https://openalex.org/W2080951048","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3048601286","https://openalex.org/W2965925734"],"abstract_inverted_index":{"Abstract":[0],"Prevention":[1],"of":[2,22,66,116,241],"colorectal":[3,10,176],"cancer":[4],"(CRC)":[5],"by":[6,187],"inspecting":[7],"and":[8,41,91,104,141,169,199,223,239],"removing":[9],"polyps":[11,48],"has":[12],"become":[13],"a":[14,76,88],"global":[15,58,110],"health":[16],"priority":[17],"because":[18],"CRC":[19],"is":[20,100],"one":[21],"the":[23,28,62,122,144,153,161,167,180,208,216,221,237,242],"most":[24],"frequent":[25],"cancers":[26],"in":[27,55,108,204,215,225],"world.":[29],"Although":[30],"recent":[31],"U-Net-based":[32,50],"convolutional":[33,67,124],"neural":[34],"networks":[35],"(CNNs)":[36],"with":[37,183],"deep":[38,78],"feature":[39,128,132,136,171],"representation":[40],"skip":[42],"connections":[43],"have":[44,53],"shown":[45],"to":[46,61,101,130,173,235],"segment":[47,175],"effectively,":[49],"approaches":[51],"still":[52],"limitations":[54],"modeling":[56],"explicit":[57],"contexts,":[59],"due":[60],"intrinsic":[63],"nature":[64],"locality":[65],"operations.":[68],"To":[69],"overcome":[70],"these":[71],"problems,":[72],"this":[73],"study":[74,232],"proposes":[75],"novel":[77],"learning":[79],"model,":[80],"SwinE-Net,":[81],"for":[82,134,157],"polyp":[83,155,227],"segmentation":[84,107],"that":[85,207],"effectively":[86],"combines":[87],"CNN-based":[89],"EfficientNet":[90],"Vision":[92],"Transformer":[93],"(ViT)-based":[94],"Swin":[95,119],"Ttransformer.":[96],"The":[97,201,245],"main":[98],"challenge":[99],"conduct":[102],"accurate":[103],"robust":[105],"medical":[106],"maintaining":[109],"semantics":[111],"without":[112],"sacrificing":[113],"low-level":[114],"features":[115,156],"CNNs":[117],"through":[118],"Transformer.":[120],"First,":[121],"multidilation":[123],"block":[125,147],"generates":[126],"refined":[127,154,168],"maps":[129,137,172],"enhance":[131],"discriminability":[133],"multilevel":[135],"extracted":[138],"from":[139,152],"CNN":[140],"ViT.":[142],"Then,":[143],"multifeature":[145],"aggregation":[146],"creates":[148],"intermediate":[149],"side":[150],"outputs":[151],"efficient":[158],"training.":[159],"Finally,":[160],"attentive":[162],"deconvolutional":[163],"network-based":[164],"decoder":[165],"upsamples":[166],"combined":[170],"accurately":[174],"polyps.":[177],"We":[178],"compared":[179],"proposed":[181,209,243,246],"approach":[182,210,247],"previous":[184,249],"state-of-the-art":[185],"methods":[186],"evaluating":[188],"various":[189],"metrics":[190],"using":[191],"five":[192],"public":[193],"datasets":[194],"(Kvasir,":[195],"ClinicDB,":[196],"ColonDB,":[197],"ETIS,":[198],"EndoScene).":[200],"comparative":[202],"evaluation,":[203],"particular,":[205],"proved":[206],"showed":[211],"much":[212],"better":[213],"performance":[214],"unseen":[217],"dataset,":[218],"which":[219],"shows":[220],"generalization":[222],"scalability":[224],"conducting":[226],"segmentation.":[228],"Furthermore,":[229],"an":[230],"ablation":[231],"was":[233],"performed":[234],"prove":[236],"novelty":[238],"advantage":[240],"network.":[244],"outperformed":[248],"studies.":[250]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":28},{"year":2023,"cited_by_count":27},{"year":2022,"cited_by_count":13}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}