{"id":"https://openalex.org/W7125181214","doi":"https://doi.org/10.48550/arxiv.2601.13954","title":"DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging","display_name":"DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging","publication_year":2026,"publication_date":"2026-01-20","ids":{"openalex":"https://openalex.org/W7125181214","doi":"https://doi.org/10.48550/arxiv.2601.13954"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.13954","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.13954","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.13954","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070628863","display_name":"Adrien Meyer","orcid":"https://orcid.org/0009-0000-8654-2270"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Meyer, Adrien","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101954770","display_name":"Didier Mutter","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mutter, Didier","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123514945","display_name":"Nicolas Padoy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Padoy, Nicolas","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5070628863"],"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/T10036","display_name":"Advanced Neural Network Applications","score":0.5781000256538391,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.5781000256538391,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.1688999980688095,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.028999999165534973,"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/annotation","display_name":"Annotation","score":0.7199000120162964},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.7052000164985657},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.6606000065803528},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6093000173568726},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.602400004863739},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5098999738693237},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4837999939918518},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.45649999380111694},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.453900009393692}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7728000283241272},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.7199000120162964},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.7052000164985657},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6930000185966492},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.6606000065803528},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6093000173568726},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.602400004863739},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5098999738693237},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4837999939918518},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48019999265670776},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.45649999380111694},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.453900009393692},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.37940001487731934},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3416000008583069},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33070001006126404},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30169999599456787},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2904999852180481},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2752000093460083}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.13954","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.13954","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.13954","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.13954","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":"article"},"sustainable_development_goals":[{"score":0.5677367448806763,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Detecting":[0],"anatomical":[1],"landmarks":[2],"in":[3,145],"medical":[4,71,107,201],"imaging":[5],"is":[6],"essential":[7],"for":[8,106],"diagnosis":[9],"and":[10,82,135,153,161,206],"intervention":[11],"guidance.":[12],"However,":[13],"object":[14,80,117],"detection":[15,219],"models":[16],"rely":[17],"on":[18,53],"costly":[19],"bounding":[20,88],"box":[21,89],"annotations,":[22],"limiting":[23],"scalability.":[24],"Weakly":[25],"Semi-Supervised":[26],"Object":[27],"Detection":[28],"(WSSOD)":[29],"with":[30,37,99,122],"point":[31,60,133,185],"annotations":[32,61,115],"proposes":[33],"annotating":[34],"each":[35],"instance":[36,162],"a":[38,54,67,101,175],"single":[39],"point,":[40],"minimizing":[41],"annotation":[42,190,214],"time":[43],"while":[44,216],"preserving":[45],"localization":[46],"signals.":[47],"A":[48],"Point-to-Box":[49,103],"teacher":[50],"model,":[51],"trained":[52],"small":[55],"box-labeled":[56],"subset,":[57],"converts":[58],"these":[59,93],"into":[62],"pseudo-box":[63],"labels":[64,137],"to":[65,138,164,189,212],"train":[66],"student":[68],"detector.":[69],"Yet,":[70],"imagery":[72],"presents":[73],"unique":[74],"challenges,":[75,94],"including":[76],"overlapping":[77,170],"anatomy,":[78],"variable":[79],"sizes,":[81],"elusive":[83],"structures,":[84,147],"which":[85,128,179],"hinder":[86],"accurate":[87],"inference.":[90],"To":[91,142],"overcome":[92],"we":[95,173],"introduce":[96],"DExTeR":[97,112,192],"(DETR":[98],"Experts),":[100,158],"transformer-based":[102],"regressor":[104],"tailored":[105],"imaging.":[108],"Built":[109],"upon":[110],"Point-DETR,":[111],"encodes":[113],"single-point":[114],"as":[116],"queries,":[118],"refining":[119],"feature":[120],"extraction":[121],"the":[123],"proposed":[124],"class-guided":[125],"deformable":[126],"attention,":[127],"guides":[129],"attention":[130],"sampling":[131],"using":[132],"coordinates":[134],"class":[136,160],"capture":[139],"class-specific":[140],"characteristics.":[141],"improve":[143],"discrimination":[144],"complex":[146],"it":[148],"introduces":[149],"CLICK-MoE":[150],"(CLass,":[151],"Instance,":[152],"Common":[154],"Knowledge":[155],"Mixture":[156],"of":[157],"decoupling":[159],"representations":[163],"reduce":[165,213],"confusion":[166],"among":[167],"adjacent":[168],"or":[169],"instances.":[171],"Finally,":[172],"implement":[174],"multi-point":[176],"training":[177],"strategy":[178],"promotes":[180],"prediction":[181],"consistency":[182],"across":[183,196],"different":[184,200],"placements,":[186],"improving":[187],"robustness":[188],"variability.":[191],"achieves":[193],"state-of-the-art":[194],"performance":[195],"three":[197],"datasets":[198],"spanning":[199],"domains":[202],"(endoscopy,":[203],"chest":[204],"X-rays,":[205],"endoscopic":[207],"ultrasound)":[208],"highlighting":[209],"its":[210],"potential":[211],"costs":[215],"maintaining":[217],"high":[218],"accuracy.":[220]},"counts_by_year":[],"updated_date":"2026-01-22T23:33:04.759266","created_date":"2026-01-22T00:00:00"}