{"id":"https://openalex.org/W7127966575","doi":"https://doi.org/10.48550/arxiv.2602.05426","title":"Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applications","display_name":"Multi-AD: Cross-Domain Unsupervised Anomaly Detection for Medical and Industrial Applications","publication_year":2026,"publication_date":"2026-02-05","ids":{"openalex":"https://openalex.org/W7127966575","doi":"https://doi.org/10.48550/arxiv.2602.05426"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.05426","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.05426","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2602.05426","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071494724","display_name":"Wahyu Rahmaniar","orcid":"https://orcid.org/0000-0002-6902-5455"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Rahmaniar, Wahyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100618207","display_name":"Kenji Suzuki","orcid":"https://orcid.org/0000-0003-1736-5404"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suzuki, Kenji","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071494724"],"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9873999953269958,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9873999953269958,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.0012000000569969416,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.0010000000474974513,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7656999826431274},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5934000015258789},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5338000059127808},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.5076000094413757},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4997999966144562},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48019999265670776},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47510001063346863},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4498000144958496}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7656999826431274},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7106000185012817},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7063999772071838},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5934000015258789},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5338000059127808},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.5076000094413757},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4997999966144562},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48019999265670776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47510001063346863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4690999984741211},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4498000144958496},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4456000030040741},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43549999594688416},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4146000146865845},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3801000118255615},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3107999861240387},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2777999937534332}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.05426","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.05426","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.05426","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.05426","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5620149374008179,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Traditional":[0],"deep":[1],"learning":[2,96],"models":[3],"often":[4],"lack":[5],"annotated":[6],"data,":[7],"especially":[8],"in":[9,23,28],"cross-domain":[10],"applications":[11],"such":[12,175],"as":[13,170,172,176],"anomaly":[14,46,153],"detection,":[15],"which":[16],"is":[17],"critical":[18],"for":[19,43,200,204,208,213,217,223],"early":[20],"disease":[21],"diagnosis":[22],"medicine":[24],"and":[25,50,77,102,118,167,206,210,215],"defect":[26],"detection":[27,47],"industry.":[29],"To":[30],"address":[31],"this":[32],"challenge,":[33],"we":[34],"propose":[35],"Multi-AD,":[36],"a":[37],"convolutional":[38],"neural":[39],"network":[40,108],"(CNN)":[41],"model":[42,69,131],"robust":[44],"unsupervised":[45],"across":[48,182],"medical":[49,160,205,214],"industrial":[51,173],"images.":[52],"Our":[53],"approach":[54,190],"employs":[55],"the":[56,68,73,88,91,98,106,111,122,129,196],"squeeze-and-excitation":[57],"(SE)":[58],"block":[59],"to":[60,70,90,114,151],"enhance":[61,152],"feature":[62],"extraction":[63],"via":[64],"channel-wise":[65],"attention,":[66],"enabling":[67,94],"focus":[71],"on":[72,158],"most":[74],"relevant":[75],"features":[76,86,147],"detect":[78,133],"subtle":[79],"anomalies.":[80],"Knowledge":[81],"distillation":[82],"(KD)":[83],"transfers":[84],"informative":[85],"from":[87],"teacher":[89],"student":[92,130],"model,":[93],"effective":[95,222],"of":[97,135,145],"differences":[99],"between":[100,116],"normal":[101,117],"anomalous":[103,119],"data.":[104,120],"Then,":[105],"discriminator":[107],"further":[109],"enhances":[110],"model's":[112],"capacity":[113],"distinguish":[115],"At":[121],"inference":[123],"stage,":[124],"by":[125],"integrating":[126],"multi-scale":[127],"features,":[128],"can":[132],"anomalies":[134],"varying":[136],"sizes.":[137],"The":[138],"teacher-student":[139],"(T-S)":[140],"architecture":[141],"ensures":[142],"consistent":[143],"representation":[144],"high-dimensional":[146],"while":[148],"adapting":[149],"them":[150],"detection.":[154],"Multi-AD":[155],"was":[156],"evaluated":[157],"several":[159],"datasets,":[161,174],"including":[162],"brain":[163],"MRI,":[164],"liver":[165],"CT,":[166],"retina":[168],"OCT,":[169],"well":[171],"MVTec":[177],"AD,":[178],"demonstrating":[179],"strong":[180],"generalization":[181],"multiple":[183],"domains.":[184],"Experimental":[185],"results":[186],"demonstrated":[187],"that":[188],"our":[189],"consistently":[191],"outperformed":[192],"state-of-the-art":[193],"models,":[194],"achieving":[195],"best":[197],"average":[198],"AUROC":[199],"both":[201],"image-level":[202],"(81.4%":[203],"99.6%":[207],"industrial)":[209,218],"pixel-level":[211],"(97.0%":[212],"98.4%":[216],"tasks,":[219],"making":[220],"it":[221],"real-world":[224],"applications.":[225]},"counts_by_year":[],"updated_date":"2026-02-07T06:15:42.627816","created_date":"2026-02-07T00:00:00"}
