{"id":"https://openalex.org/W7155396688","doi":"https://doi.org/10.48550/arxiv.2604.20213","title":"Weighted Knowledge Distillation for Semi-Supervised Segmentation of Maxillary Sinus in Panoramic X-ray Images","display_name":"Weighted Knowledge Distillation for Semi-Supervised Segmentation of Maxillary Sinus in Panoramic X-ray Images","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155396688","doi":"https://doi.org/10.48550/arxiv.2604.20213"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20213","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20213","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.20213","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102413048","display_name":"Juha Park","orcid":"https://orcid.org/0009-0004-7521-609X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Juha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134401652","display_name":"Jiho Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Jiho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031643443","display_name":"Jong Pil Yun","orcid":"https://orcid.org/0000-0002-2802-9978"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yun, Jong Pil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134407229","display_name":"Yong Chan Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Yong Chan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034489814","display_name":"Han-Gyeol Yeom","orcid":"https://orcid.org/0000-0002-5608-822X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yeom, Han-Gyeol","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134421351","display_name":"Byung Do Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Byung Do","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100620529","display_name":"Sang Jun Lee","orcid":"https://orcid.org/0000-0002-9312-6299"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Sang Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11363","display_name":"Dental Radiography and Imaging","score":0.7382000088691711,"subfield":{"id":"https://openalex.org/subfields/3504","display_name":"Oral Surgery"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11363","display_name":"Dental Radiography and Imaging","score":0.7382000088691711,"subfield":{"id":"https://openalex.org/subfields/3504","display_name":"Oral Surgery"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10984","display_name":"Sinusitis and nasal conditions","score":0.15049999952316284,"subfield":{"id":"https://openalex.org/subfields/2733","display_name":"Otorhinolaryngology"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.0142000000923872,"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":0.8302000164985657},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5532000064849854},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5209000110626221},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.515999972820282},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.46889999508857727},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43540000915527344},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4194999933242798},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.382099986076355}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8302000164985657},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7558000087738037},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7322999835014343},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5712000131607056},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5532000064849854},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5209000110626221},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.515999972820282},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.46889999508857727},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4194999933242798},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.382099986076355},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.3529999852180481},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30410000681877136},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2987000048160553},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2858999967575073},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2590999901294708}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20213","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20213","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.20213","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20213","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.8079866766929626,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"segmentation":[1,56,67,189,214],"of":[2,41,55,124,151,195],"maxillary":[3],"sinus":[4],"in":[5,23],"panoramic":[6,35,76,173],"X-ray":[7,174],"images":[8,175],"is":[9,81,136],"essential":[10],"for":[11,224],"dental":[12,24,226],"diagnosis":[13],"and":[14,37,53,74,116,153,179,211],"surgical":[15],"planning;":[16],"however,":[17],"this":[18],"task":[19],"remains":[20],"relatively":[21],"underexplored":[22],"imaging":[25],"research.":[26],"Structural":[27],"overlap,":[28],"ambiguous":[29],"anatomical":[30],"boundaries":[31,152],"inherent":[32],"to":[33,83,105],"two-dimensional":[34],"projections,":[36],"the":[38,51,122,129,149,167,184,192,205],"limited":[39,217],"availability":[40],"large":[42],"scale":[43],"clinical":[44,172],"datasets":[45],"with":[46,88],"reliable":[47,89],"pixel-level":[48],"annotations":[49],"make":[50],"development":[52],"evaluation":[54],"models":[57],"challenging.":[58],"To":[59,119,165],"address":[60],"these":[61],"challenges,":[62],"we":[63,98,132,170],"propose":[64],"a":[65,85,94,100,137],"semi-supervised":[66,163,207],"framework":[68,208],"that":[69,183,204],"effectively":[70],"leverages":[71],"both":[72],"labeled":[73,218],"unlabeled":[75,160],"radiographs,":[77],"where":[78],"knowledge":[79,102],"distillation":[80,103,108],"utilized":[82],"train":[84],"student":[86,117],"model":[87],"structural":[90,112],"information":[91],"distilled":[92],"from":[93,159,176],"teacher":[95,115,130],"model.":[96],"Specifically,":[97],"introduce":[99,133],"weighted":[101],"loss":[104],"suppress":[106],"unreliable":[107],"signals":[109],"caused":[110],"by":[111,128],"discrepancies":[113],"between":[114],"predictions.":[118],"further":[120],"enhance":[121],"quality":[123],"pseudo":[125],"labels":[126],"generated":[127],"network,":[131],"SinusCycle-GAN":[134],"which":[135],"refinement":[138,146],"network":[139],"based":[140],"on":[141],"unpaired":[142],"image-to-image":[143],"translation.":[144],"This":[145],"process":[147],"improves":[148],"precision":[150],"reduces":[154],"noise":[155],"propagation":[156],"when":[157],"learning":[158],"data":[161,219],"during":[162],"training.":[164],"evaluate":[166],"proposed":[168,185,206],"method,":[169],"collected":[171],"2,511":[177],"patients,":[178],"experimental":[180],"results":[181,202],"demonstrate":[182],"method":[186],"outperforms":[187],"state-of-the-art":[188],"models,":[190],"achieving":[191],"Dice":[193],"score":[194],"96.35\\%":[196],"while":[197],"reducing":[198],"boundary":[199],"error.":[200],"The":[201],"indicate":[203],"provides":[209],"robust":[210],"anatomically":[212],"consistent":[213],"performance":[215],"under":[216],"conditions,":[220],"highlighting":[221],"its":[222],"potential":[223],"broader":[225],"image":[227],"analysis":[228],"applications.":[229]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-24T00:00:00"}