{"id":"https://openalex.org/W7147155123","doi":"https://doi.org/10.1109/cw68232.2025.00043","title":"Swin-WNet: Boundary-Aware Semantic Segmentation for Oral Squamous Cell Carcinoma","display_name":"Swin-WNet: Boundary-Aware Semantic Segmentation for Oral Squamous Cell Carcinoma","publication_year":2025,"publication_date":"2025-10-14","ids":{"openalex":"https://openalex.org/W7147155123","doi":"https://doi.org/10.1109/cw68232.2025.00043"},"language":null,"primary_location":{"id":"doi:10.1109/cw68232.2025.00043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cw68232.2025.00043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Cyberworlds (CW\uff09","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/A5059538454","display_name":"Z. F. Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhenfei Wang","raw_affiliation_strings":["Graduate School of Engineering, University of Yamanashi,Kofu,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, University of Yamanashi,Kofu,Japan","institution_ids":["https://openalex.org/I66906201"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001286007","display_name":"Zhenyang Zhu","orcid":"https://orcid.org/0000-0003-1023-3193"},"institutions":[{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhenyang Zhu","raw_affiliation_strings":["University of Yamanashi,Faculty of Engineering,Department of Computer Science,Kofu,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Yamanashi,Faculty of Engineering,Department of Computer Science,Kofu,Japan","institution_ids":["https://openalex.org/I66906201"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049974608","display_name":"Kunio Yoshizawa","orcid":"https://orcid.org/0000-0003-4779-9940"},"institutions":[{"id":"https://openalex.org/I4210112011","display_name":"University of Yamanashi Hospital","ror":"https://ror.org/022tqjv17","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210112011"]},{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kunio Yoshizawa","raw_affiliation_strings":["University of Yamanashi,Department of Oral and Maxillofacial Surgery,Chuo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Yamanashi,Department of Oral and Maxillofacial Surgery,Chuo,Japan","institution_ids":["https://openalex.org/I4210112011","https://openalex.org/I66906201"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048048944","display_name":"Masahiro Toyoura","orcid":"https://orcid.org/0000-0002-5897-7573"},"institutions":[{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiro Toyoura","raw_affiliation_strings":["University of Yamanashi,Faculty of Engineering,Department of Computer Science,Kofu,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Yamanashi,Faculty of Engineering,Department of Computer Science,Kofu,Japan","institution_ids":["https://openalex.org/I66906201"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081460244","display_name":"Naoki Oishi","orcid":"https://orcid.org/0000-0002-9365-1093"},"institutions":[{"id":"https://openalex.org/I4210112011","display_name":"University of Yamanashi Hospital","ror":"https://ror.org/022tqjv17","country_code":"JP","type":"healthcare","lineage":["https://openalex.org/I4210112011"]},{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Naoki Oishi","raw_affiliation_strings":["University of Yamanashi,Department of Pathology,Chuo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Yamanashi,Department of Pathology,Chuo,Japan","institution_ids":["https://openalex.org/I4210112011","https://openalex.org/I66906201"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5132634564","display_name":"Xiaoyang Mao","orcid":null},"institutions":[{"id":"https://openalex.org/I66906201","display_name":"University of Yamanashi","ror":"https://ror.org/059x21724","country_code":"JP","type":"education","lineage":["https://openalex.org/I66906201"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xiaoyang Mao","raw_affiliation_strings":["University of Yamanashi,Faculty of Engineering,Department of Computer Science,Kofu,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Yamanashi,Faculty of Engineering,Department of Computer Science,Kofu,Japan","institution_ids":["https://openalex.org/I66906201"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"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":0.70031848,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"08"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.4368000030517578,"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"}},"topics":[{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.4368000030517578,"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/T10862","display_name":"AI in cancer detection","score":0.10779999941587448,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.09319999814033508,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6747000217437744},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.659600019454956},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5472999811172485},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5271999835968018},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46129998564720154},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.44600000977516174},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43630000948905945},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4255000054836273},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4221999943256378}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6747000217437744},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.659600019454956},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6071000099182129},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.550599992275238},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5472999811172485},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5271999835968018},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46129998564720154},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.44600000977516174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43630000948905945},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4221999943256378},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3774000108242035},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3508000075817108},{"id":"https://openalex.org/C3019992690","wikidata":"https://www.wikidata.org/wiki/Q92767510","display_name":"Basal cell","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cw68232.2025.00043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cw68232.2025.00043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Cyberworlds (CW\uff09","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W183625566","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2016081624","https://openalex.org/W2057882522","https://openalex.org/W2150769593","https://openalex.org/W2560023338","https://openalex.org/W2884436604","https://openalex.org/W2888358068","https://openalex.org/W2910628332","https://openalex.org/W2962914239","https://openalex.org/W2963150697","https://openalex.org/W2963803174","https://openalex.org/W2964309882","https://openalex.org/W2981994674","https://openalex.org/W2989684653","https://openalex.org/W3096653763","https://openalex.org/W3118493528","https://openalex.org/W3127595489","https://openalex.org/W3138516171","https://openalex.org/W3170841864","https://openalex.org/W3204614423","https://openalex.org/W3211490618","https://openalex.org/W4214835866","https://openalex.org/W4221163766","https://openalex.org/W4283828743","https://openalex.org/W4295937532","https://openalex.org/W4312815172","https://openalex.org/W4319300975","https://openalex.org/W4321232185","https://openalex.org/W4390874575","https://openalex.org/W4400881081","https://openalex.org/W4403067018","https://openalex.org/W4403150357","https://openalex.org/W4403152480"],"related_works":[],"abstract_inverted_index":{"Oral":[0],"squamous":[1],"cell":[2],"carcinoma":[3],"(OSCC)":[4],"poses":[5],"a":[6,41,121,142],"significant":[7],"threat":[8],"to":[9,13,48,62,106,119,157,162,178],"public":[10,225],"health":[11],"due":[12,105],"its":[14,80],"severity":[15],"and":[16,35,54,79,86,112,149,152,189,199,223],"the":[17,37,57,64,138,159,168,171,174,193,196,204,211],"laborintensive":[18],"process":[19],"of":[20,40,59,66,68,170,195,206,213],"image":[21],"analysis":[22],"required":[23],"by":[24,135],"physicians.":[25],"Intercellular":[26],"bridges":[27,45,61],"are":[28,46],"bridge-like":[29],"structures":[30],"that":[31,123,230],"connect":[32],"adjacent":[33],"cells":[34],"indicate":[36],"differentiation":[38,51,67],"level":[39],"cancer.":[42,69],"Although":[43],"intercellular":[44,60,99],"known":[47],"disappear":[49],"as":[50,77],"decreases,":[52],"pathologists":[53],"clinicians":[55],"evaluate":[56],"presence":[58],"assess":[63],"degree":[65],"While":[70],"state-of-the-art":[71],"(SOTA)":[72],"deep":[73],"learning":[74],"methods,":[75],"such":[76],"U-Net":[78],"variants,":[81],"perform":[82],"well":[83],"on":[84,220],"uniform":[85],"clearly":[87],"delineated":[88],"objects":[89,97],"(e.g.,":[90,98],"cell,":[91],"lung,":[92],"etc.),":[93],"accurately":[94],"segmenting":[95],"intricate":[96,130,164],"bridge,":[100],"retinal":[101],"vessel)":[102],"remains":[103],"challenging":[104],"their":[107],"complex":[108],"topologies,":[109],"fine":[110],"branches,":[111],"irregular":[113],"morphological":[114],"changes.":[115],"This":[116],"paper":[117],"aims":[118],"propose":[120],"method":[122,232],"effectively":[124],"utilize":[125],"boundary":[126,150,172,197],"information,":[127],"particularly":[128],"targeting":[129],"objects.":[131,165],"Our":[132],"approach,":[133],"inspired":[134],"Swin-UNet,":[136],"employs":[137],"Swin":[139],"Transformer,":[140],"comprising":[141],"feature":[143,175],"encoder,":[144],"two":[145],"decoders":[146],"(semantic":[147],"decoder":[148,198,201],"decoder)":[151],"an":[153],"attention-guided":[154],"fusion":[155],"module":[156],"enhance":[158],"model's":[160],"ability":[161,177],"segment":[163],"By":[166],"applying":[167],"constraints":[169],"decoder,":[173],"encoder's":[176],"encode":[179],"structural":[180,207],"information":[181,187],"is":[182],"enhanced":[183],"without":[184],"compromising":[185],"semantic":[186,200],"extraction":[188],"representation.":[190],"Furthermore,":[191],"fusing":[192],"outputs":[194],"further":[202],"strengthens":[203],"detail":[205],"information.":[208],"To":[209],"validate":[210],"generalizability":[212],"our":[214,231],"method,":[215],"we":[216],"conducted":[217],"comparative":[218],"experiments":[219],"one":[221,224],"private":[222],"dataset.":[226],"The":[227],"results":[228],"demonstrate":[229],"outperforms":[233],"SOTA":[234],"methods.":[235]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-02T00:00:00"}
