{"id":"https://openalex.org/W4400067005","doi":"https://doi.org/10.1145/3663976.3664239","title":"EDO-SANet: Shape-Aware Network with Edge Detection Operator for Polyp Segmentation","display_name":"EDO-SANet: Shape-Aware Network with Edge Detection Operator for Polyp Segmentation","publication_year":2024,"publication_date":"2024-04-26","ids":{"openalex":"https://openalex.org/W4400067005","doi":"https://doi.org/10.1145/3663976.3664239"},"language":"en","primary_location":{"id":"doi:10.1145/3663976.3664239","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3663976.3664239","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition","raw_type":"proceedings-article"},"type":"article","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/A5099583380","display_name":"Yifei Zhao","orcid":"https://orcid.org/0009-0007-9152-959X"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yifei Zhao","raw_affiliation_strings":["North China Electric Power University, China"],"raw_orcid":"https://orcid.org/0009-0007-9152-959X","affiliations":[{"raw_affiliation_string":"North China Electric Power University, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041735204","display_name":"Xiaoying Wang","orcid":"https://orcid.org/0009-0005-8651-7856"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoying Wang","raw_affiliation_strings":["North China Electric Power University, China"],"raw_orcid":"https://orcid.org/0009-0005-8651-7856","affiliations":[{"raw_affiliation_string":"North China Electric Power University, China","institution_ids":["https://openalex.org/I153473198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100421256","display_name":"Yang Li","orcid":"https://orcid.org/0000-0001-5543-8171"},"institutions":[{"id":"https://openalex.org/I184983240","display_name":"Northeast Normal University","ror":"https://ror.org/02rkvz144","country_code":"CN","type":"education","lineage":["https://openalex.org/I184983240"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Li","raw_affiliation_strings":["Northeast Normal University, China"],"raw_orcid":"https://orcid.org/0000-0001-5543-8171","affiliations":[{"raw_affiliation_string":"Northeast Normal University, China","institution_ids":["https://openalex.org/I184983240"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101678893","display_name":"Junping Yin","orcid":"https://orcid.org/0000-0002-9270-2071"},"institutions":[{"id":"https://openalex.org/I4210145278","display_name":"Institute of Applied Physics and Computational Mathematics","ror":"https://ror.org/03sxpbt26","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210145278"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junping Yin","raw_affiliation_strings":["Institute of Applied Physics and Computational, China"],"raw_orcid":"https://orcid.org/0000-0002-9270-2071","affiliations":[{"raw_affiliation_string":"Institute of Applied Physics and Computational, China","institution_ids":["https://openalex.org/I4210145278"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14386192,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12707","display_name":"Vehicle License Plate Recognition","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9751999974250793,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12111","display_name":"Industrial Vision Systems and Defect Detection","score":0.9646999835968018,"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":0.6523809432983398},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5773308277130127},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.571753740310669},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5648452043533325},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5359153747558594},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5242538452148438},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.4463191330432892},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4442235827445984},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40900224447250366},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2805919051170349},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.228605717420578}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6523809432983398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5773308277130127},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.571753740310669},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5648452043533325},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5359153747558594},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5242538452148438},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.4463191330432892},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4442235827445984},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40900224447250366},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2805919051170349},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.228605717420578},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3663976.3664239","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3663976.3664239","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8758758964","display_name":null,"funder_award_id":"NSFC12031016","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2008359794","https://openalex.org/W2017745767","https://openalex.org/W2021088830","https://openalex.org/W2145023731","https://openalex.org/W2194775991","https://openalex.org/W2285968993","https://openalex.org/W2317582029","https://openalex.org/W2601564443","https://openalex.org/W2884436604","https://openalex.org/W2890084211","https://openalex.org/W2932083555","https://openalex.org/W2962834855","https://openalex.org/W2962927567","https://openalex.org/W2966926453","https://openalex.org/W2980998394","https://openalex.org/W2997286550","https://openalex.org/W2999580839","https://openalex.org/W3081752372","https://openalex.org/W3092344722","https://openalex.org/W3127751679","https://openalex.org/W3147142721","https://openalex.org/W3154575015","https://openalex.org/W3162386519","https://openalex.org/W3175515048","https://openalex.org/W3177634011","https://openalex.org/W3196075503","https://openalex.org/W3204166336","https://openalex.org/W3204995672","https://openalex.org/W4238100585","https://openalex.org/W4287225564","https://openalex.org/W4311033561","https://openalex.org/W4312122089","https://openalex.org/W4312785900","https://openalex.org/W4316021925","https://openalex.org/W4318767606","https://openalex.org/W4319165983","https://openalex.org/W4324314131","https://openalex.org/W4353007132","https://openalex.org/W4363650233","https://openalex.org/W4382877880","https://openalex.org/W4385245566","https://openalex.org/W4386076222"],"related_works":["https://openalex.org/W2028037572","https://openalex.org/W2315652488","https://openalex.org/W2039365229","https://openalex.org/W1522196789","https://openalex.org/W2145843506","https://openalex.org/W2379089757","https://openalex.org/W2056407677","https://openalex.org/W4293054829","https://openalex.org/W1999737656","https://openalex.org/W2128085731"],"abstract_inverted_index":{"Automatic":[0],"and":[1,15,64,71,132,138,157],"accurate":[2],"segmentation":[3,163],"of":[4,17,87,96,115,168],"colonic":[5],"polyps":[6,18,28],"can":[7],"effectively":[8],"help":[9],"physicians":[10],"quickly":[11],"identify":[12,93],"the":[13,24,33,53,60,85,94,98,113],"size":[14],"location":[16],"during":[19],"endoscopy.":[20],"However,":[21],"due":[22],"to":[23,32,37,68,91,106,111,129,145,171],"variable":[25],"intestinal":[26],"environment,":[27],"are":[29],"highly":[30],"similar":[31],"surrounding":[34],"tissue":[35],"leading":[36],"over-segmentation":[38],"or":[39],"under-segmentation.":[40],"To":[41],"tackle":[42],"these":[43],"concerns,":[44],"we":[45,122],"propose":[46],"a":[47,56,124],"novel":[48],"network":[49,54],"with":[50],"shape-awareness.":[51],"Specifically,":[52],"employs":[55],"dual-stream":[57],"encoder,":[58],"including":[59],"mainstream":[61],"Transoformer-based":[62],"encoder":[63,67,76],"an":[65],"auxiliary":[66,75],"extract":[69],"global":[70,137],"local":[72,139],"representations.":[73],"The":[74,165,181],"is":[77,104,175,183],"built":[78],"on":[79,160],"deformable":[80],"convolutional":[81],"v3":[82],"for":[83],"sensing":[84],"shape":[86],"polyps.":[88],"In":[89],"order":[90],"better":[92,130,152],"edges":[95],"polyps,":[97],"Edge":[99],"Detection":[100],"Guided":[101],"Module":[102,127],"(EDGM)":[103],"proposed":[105],"utilize":[107],"edge":[108,116],"detection":[109],"operators":[110],"highlight":[112],"importance":[114],"information":[117],"in":[118,141,155],"low-level":[119],"features.":[120,143],"Furthermore,":[121],"introduce":[123],"Semantic":[125],"Interaction":[126],"(SIM)":[128],"localize":[131],"calibrate":[133],"targets":[134],"by":[135],"integrating":[136],"semantics":[140],"high-level":[142],"Compared":[144],"other":[146,172],"state-of-the-art":[147],"methods,":[148],"our":[149,169],"model":[150,170],"achieves":[151],"performance":[153],"both":[154],"learning":[156],"generalization":[158],"ability":[159],"five":[161],"polyp":[162],"datasets.":[164],"potential":[166],"applicability":[167],"biomedical":[173],"fields":[174],"demonstrated":[176],"through":[177],"its":[178],"outstanding":[179],"performance.":[180],"code":[182],"available":[184],"at":[185],"https://github.com/xff12138/EDO-SANet":[186]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
